packagesList <- c("data.table", "tidyr", "parallel", "igraph", "ggplot2", "Hmisc", "corrplot", "dplyr", "pheatmap", "plotfunctions", "reticulate", "tidyselect")
newPackages <- packagesList[!(packagesList %in% installed.packages()[,"Package"])]
if(length(newPackages) > 0) {
  install.packages(newPackages)
}

packagesListBC <- c("GSEABase",  "GOstats", "GenomicRanges")
newPackagesBC <- packagesListBC[!(packagesListBC %in% installed.packages()[,"Package"])]
if(length(newPackagesBC) > 0) {
  BiocManager::install(newPackagesBC)
}
library("data.table")
library("tidyr")
library("parallel")
library("igraph")
library("Hmisc")
library("corrplot")
library("dplyr")
library("GSEABase")
library("GOstats")
library("pheatmap")
# library("reticulate")
library("plotfunctions")
library("tidyselect")
library("GenomicRanges")
if (!file.exists("data/SI_Data_01_expressionValues.txt") | !file.exists("data/SI_Data_03_genotypes.txt") | !file.exists("data/SI_Data_04_eQTL.csv")) {
  files_in_zip <- unzip(zipfile = "data/zipped_data.zip", list = T)
  for (datafile in files_in_zip$Name){
    if (!file.exists(paste0("data/", "datafile"))){
      unzip(zipfile = "data/zipped_data.zip", exdir = "data/")
    }
  }
}

Data from Albert FW, Bloom JS, Siegel J, Day L, Kruglyak L. 2018. Genetics of trans-regulatory variation in gene expression. eLife 7: 1–39. Source data or source data
Files to download:
* SI_Data_01_expressionValues.txt (in the repo) * SI_Data_03_genotypes.txt (too big for the repo) * SI_Data_04_eQTL.csv

I started with :
* phenotype matrix - contains the gene expression data (expression levels in units of log2(TPM) for all genes and segregants)
* genotype matrix - contains the genotype information (genotypes at 42,052 markers for all segregants. BY (i.e. reference) alleles are denoted by ‘−1’. RM alleles are denoted by ‘1’.)
* eqtl_results - Genes with a local eQTL and significant Allele-specific expression (ASE), and discordant direction of effect. (1) Positive values indicate higher expression in RM compared to BY. (2) Shown is the less sig- nificant p-value from the two ASE datasets. (3) The table shows only genes where both ASE datasets agreed in the direction of effect. Shown is the average effect.

1 for the actual analysis jump to “Inferring causality with new parameters”

Original parameters used

var.exp.lim <- 0.1

nSNPs <- length(colnames(genotype)) - 1
nGenes <- length(colnames(phenotype)) - 1
# nSNPs <- 42052
# nGenes <- 5720

snp.pval <- 0.05 / (as.numeric(nGenes) * as.numeric(nSNPs))
snp.pval.nsign <- as.numeric(1e-5)

corr.pval <-  0.05 / (nGenes * nGenes)

2 Get effects table

Probably best to run in uppmax

What’s happening here:
- create a table with all the possible combinations of geneA-eqtlA with geneB-eqtlB
- ANOVA to test the effect of an eQTL on a gene
- combines the anova results with the table with the geneA-eqtlA and geneB-eqtlB combinations
- get correlation between genes
- adds the correlation between genes to the previous table
Output is a table with all combinations of gene-eqtl pairs (all geneA must have a cis-eqtl but not all of geneB have to have an eqtl (in cis or not)), if their eqtl is in cis or not, the variance explained by each gene-eqtl pair, the anova p-value and r2 of the effect of an eqtl on a gene and the correlation value and p-value between genes.

library(data.table)
if (!file.exists("results/effects_table.Rdata")){
  
  eqtl_results.sub <- eqtl_results[,.(gene, pmarker, cis, var.exp)]
  
  genesB <- colnames(phenotype[,2:ncol(phenotype)])
  effectsA_B.sepA_B <- create_ini_table(eqtl_results.sub, genesB, var.exp.lim)
  
  ### Find effect of eqtls from geneA in expression of geneB ####
  eqtls.A <- unique(effectsA_B.sepA_B$eqtl.A)
  genes.B <- unique(effectsA_B.sepA_B$geneB)
  res.tot.eqtlA_geneB <- data.table(expand.grid(gene=genes.B, eqtl=eqtls.A))#, anv.res=NA))
  res.eqtlA_geneB <- res.tot.eqtlA_geneB
  
  # run anova in parallel -- effect of eqtlA on geneB
  message("running anova for the effect of eqtlA on geneB")
  cl = makeCluster(detectCores() - 1, type="FORK")
  res.eqtlA_geneB$anv.res <- parApply(cl=cl,res.eqtlA_geneB,1,effect_eqtl_gene, phenotype, genotype)
  stopCluster(cl)

  
  ### Find effect of eqtls from geneB in expression of geneA
  message("running anova for the effect of eqtlB on geneA")
  eqtls.B <- na.omit(unique(effectsA_B.sepA_B$eqtl.B))
  genes.A <- unique(effectsA_B.sepA_B$geneA)
  res.tot.eqtlB_geneA <- data.table(expand.grid(gene=genes.A, eqtl=eqtls.B))#, anv.res=NA))
  res.eqtlB_geneA <- res.tot.eqtlB_geneA
  
  # run the anova function in parallel -- effect of eqtlB on geneA
  cl = makeCluster(detectCores() - 1, type="FORK")
  res.eqtlB_geneA$anv.res <- parApply(cl=cl,res.eqtlB_geneA,1,effect_eqtl_gene, phenotype, genotype)
  stopCluster(cl)
  
  ## merge anova results with the information table to create an effects table
  
  # results from effect of eqtlA on geneB
  effects_table.eqtlA_geneB <- merge_after_anova(res.eqtlA_geneB, "B", "A", effectsA_B.sepA_B)
  
  # results from effect of eqtlB on geneA
  effects_table.anova <- merge_after_anova(res.eqtlB_geneA, gene.AB="A", eqtl.AB="B", effects_table.eqtlA_geneB )
  setcolorder(effects_table.anova, c("geneA", "geneB", "eqtl.A", "eqtl.B", "cis.A", "cis.B"))
  
  ### correlation ####
  message("getting correlation between genes")
  cor_traits <- rcorr(as.matrix(phenotype[,2:ncol(phenotype)])) # to remove the sample names
  
  cor_traits.cor <- cor_traits$r
  cor_traits.p <- cor_traits$P
  
  my_cor_matr_flat <- flat_cor_mat(cor_traits.cor,cor_traits.p)
  my_cor_matr_flat <- data.table(my_cor_matr_flat)
  
  cor_matr <- my_cor_matr_flat[!duplicated(t(apply(my_cor_matr_flat, 1, sort))), ]
  
  # merge the correlation values with the table that has the anova results
  setnames(cor_matr, old=c("row","column", "pval"), new=c("geneA","geneB", "cor.pval"))
  
  message("combining anova results with correlation")
  effects_table.cor <- merge(effects_table.anova, cor_matr, by=c("geneA","geneB"), all.x=T)
  
  message("saving effects table with correlation between genes")
  save(effects_table, file="results/effects_table.Rdata")
  
}

2.1 Test different parameters and create summary table with results from testing the different parameters

Test different parameter values combinations to see if I there is a certain combination that gives optimal results in the causality inference.
Takes a while, probably better to use uppmax

Assumptions:
* geneA is in cis with eqtlA
* geneB is in cis with eqtlB
* var.explained for geneA must be > var.exp.lim
* correlation pval is < corr.pval
Inferred if gene A is affecting geneB or if geneB is affecting geneA.
* geneA != geneB

There are two categories and several end results:
Categories:
* A affects B: A->B
* B affects A: B->A

End results:
* A->B = T and B->A = F or A->B = F and B->A = T –> this is the case we are mostly interested in. It means we can say that a gene affects the other, but it’s not affected by it.
* A->B = T and B->A = NA or A->B = NA and B->A = T –> we can say that a gene affects the other, but we can’t say if the second gene affects the first
* A->B = NA and B->A = NA –> we can’t say anything about causality
* A->B = F and B->A = T or A->B = F and B->A = T –> neither gene affects the other
* A->B = T and B->A = T or A->B = T and B->A = T

How it works:
* A->B = T if anova p-value for the effect of eqtlA on geneB is < snp.pval
* A->B = F if the anova p-value of the effect of eqtlA on geneB is > snp.pval.nsign and geneA and geneB have different eqtls
* B->A = T if anova p-value for the effect of eqtlB on geneA is < snp.pval
* B->A = F if the anova p-value of the effect of eqtlB on geneA is > snp.pval.nsign and geneA and geneB have different eqtls

What’s happening here:
- set different values for an effect or a correlation to be significant or non-significant
- find causality (A->B and B->A) using the diferent parameters - the object “res” is a list of tables where each table corresponds to the causality inference done with different parameters (specified in each table)

geneA <- causal genes
geneB <- affected genes

  • create table with a summary of the results:
    • number of different geneA and geneB found
    • number of different eqtlA and eqtlB found
    • number of unique gene pairs
    • number of different gene-eqtl pairs for geneA-eqtlA and geneB-eqtlB
    • the parameter cutoffs
if (!file.exists("results/resparams.Rdata")){
  
  message("loading effects_table")
  load("results/effects_table.Rdata")
  
  sign_p <- c(1e-17, 1e-16, 1e-15, 1e-14, 1e-13, 1e-12, 1e-11, 1e-10, 1e-9, 1e-8, 1e-7, 1e-6, 1e-5, 1e-4, 1e-3, 1e-2)
  non_sign_p <- c(1e-7, 1e-6, 1e-5, 1e-4, 1e-3, 1e-2)
  cor_p <- c(1e-9, 1e-8, 1e-7, 1e-6, 1e-5, 1e-4, 1e-3, 1e-2)
  params0 <- data.table(expand.grid(sign_p=sign_p, non_sign_p=non_sign_p, cor_p=cor_p))
  
  message("testing parameters")
  
  cl = makeCluster(detectCores() - 1, type="FORK")
  res <- parApply(cl=cl,params0,1, testparams, effects_table, var.exp.lim)
  stopCluster(cl)

  save(res, file="results/resparams.Rdata")
  
}

if (!file.exists("results/res_table_params.Rdata")){
  message("loading resparams")
  load(file = "results/resparams.Rdata")
  
  # create summary table with results from testing the different parameters
  message("creating summary table")
  cl = makeCluster(detectCores()-1, type="FORK")
  res_table.temp <- parLapply(cl=cl, res, create_res_table)
  stopCluster(cl)
  
  res_table <- rbindlist(res_table.temp)
  res_table <- res_table[order(sign.p, -nonsign.p, cor.p)]
  
  save(res_table, file="results/res_table_params.Rdata")
  
} else if (file.exists("results/res_table_params.Rdata")){
  message("loading res_table with the results of testing the several parameters")
  load("results/res_table_params.Rdata")
}
## loading res_table with the results of testing the several parameters

2.1.1 Plot number of times geneA -> geneB with the different parameters tested

Only the cases where A->B=T and B->A=F

par(mfrow=c(2,4))

cor.pvals <- unique(res_table$cor.p)
linetype <- c(1:length(unique(res_table$nonsign.p)))

for (p in cor.pvals){
  xrange <- range(-log(res_table$sign.p)) # set x-axis range
  yrange <- range(res_table$unique.genepairs) # set y-axis range
  plot(xrange, yrange, type = "n", main=paste("corr pval = ", p), xlab = "-log(sign.p)",
       ylab = " #unique gene pairs") # empty plot
  colors <- rainbow(length(unique(res_table$nonsign.p)))
  for (i in 1:length(unique(res_table$nonsign.p))){
    x <- -log(res_table[nonsign.p==nonsign.p[i] & cor.p==p]$sign.p)
    y <- res_table[nonsign.p==nonsign.p[i] & cor.p==p]$unique.genepairs
    lines(x,y,  type="l", lwd=1.5,lty=linetype[i], col=colors[i])
  }
  #legend(x=20, yrange[2], unique(res_table$nonsign.p), col=colors, lty=linetype, cex=0.8)
}
Number of unique gene pairs found when using different cutoffs. Each pannel corresponds to a different correlation p-value cutff. X-axis is the -log(pvalue) (for the effect to be significant) and differenc colors represent different values for the non-significant p-value (for the effect to be considered non-significant)

Number of unique gene pairs found when using different cutoffs. Each pannel corresponds to a different correlation p-value cutff. X-axis is the -log(pvalue) (for the effect to be significant) and differenc colors represent different values for the non-significant p-value (for the effect to be considered non-significant) 

par(mfrow=c(1,1))

xrange <- range(-log(res_table$sign.p)) # set x-axis range
yrange <- range(res_table$unique.genepairs) # set y-axis range
plot(xrange, yrange, type = "n", xlab = "-log(p)", ylab = "#unique gene pairs",  
     main="#gene pairs where A->B") # empty plot
colors <- rainbow(length(unique(res_table$nonsign.p)))
linetype <- c(1:length(unique(res_table$nonsign.p)))
for (i in 1:length(unique(res_table$nonsign.p))){
  x <- -log(res_table$sign.p[res_table$nonsign.p==unique(res_table$nonsign.p)[i]])
  y <- res_table$unique.genepairs[res_table$nonsign.p==unique(res_table$nonsign.p)[i]]
  lines(x,y,  type="l", lwd=1.5,lty=linetype[i], col=colors[i])
}
legend(x=20, yrange[2], unique(res_table$nonsign.p), col=colors, lty=linetype, cex=0.8)
Number of unique gene pairs found when using different cutoffs. X-axis is the -log(pvalue) (for the effect to be significant) and differenc colors represent different values for the non-significant p-value (for the effect to be considered non-significant)

Number of unique gene pairs found when using different cutoffs. X-axis is the -log(pvalue) (for the effect to be significant) and differenc colors represent different values for the non-significant p-value (for the effect to be considered non-significant)

3 Inferring causality with new parameters

var.exp.lim <- 0.1

# nSNPs <- length(colnames(genotype))-1
# nGenes <- length(colnames(phenotype))-1

nSNPs <- 42052
nGenes <- 5720

snp.pval <- as.numeric(1e-5)
snp.pval.nsign <- 0.01

corr.pval <- 0.05/choose(nGenes,2)

Assumptions (same as before):
* geneA is in cis with eqtlA
* geneB is in cis with eqtlB
* var.explained for geneA must be > var.exp.lim
* correlation pval is < corr.pval
Inferred if gene A is affecting geneB or if geneB is affecting geneA.
* geneA != geneB

There are two categories and several end results:
Categories:
* A affects B: A->B
* B affects A: B->A

End results:
* A->B = T and B->A = F or A->B = F and B->A = T –> this is the case we are mostly interested in. It means we can say that a gene affects the other, but it’s not affected by it.
* A->B = T and B->A = NA or A->B = NA and B->A = T –> we can say that a gene affects the other, but we can’t say if the second gene affects the first
* A->B = NA and B->A = NA –> we can’t say anything about causality
* A->B = F and B->A = T or A->B = F and B->A = T –> neither gene affects the other
* A->B = T and B->A = T or A->B = T and B->A = T

How it works:
* A->B = T if anova p-value for the effect of eqtlA on geneB is < snp.pval
* A->B = F if the anova p-value of the effect of eqtlA on geneB is > snp.pval.nsign and geneA and geneB have different eqtls
* B->A = T if anova p-value for the effect of eqtlB on geneA is < snp.pval
* B->A = F if the anova p-value of the effect of eqtlB on geneA is > snp.pval.nsign and geneA and geneB have different eqtls

3.1 Get the effect of each gene on the other and the cases where A->B = T and B->A = F

# find all effects
if (!file.exists("results/findeffects_all_newparams.gz")){
  message("loading effects_table")
  load("results/effects_table.Rdata")
  
  message("finding causality")

  find.effects <- effects_table[cor.pval < corr.pval & cis.A ==T & cis.B==T & geneA!=geneB]
  find.effects <- find.effects_fun(find.effects, snp.pval, snp.pval.nsign)
  
  message("saving to file")
  fwrite(find.effects, "results/findeffects_all_newparams.gz")
}

# subset to get effects where A affects B and B does not affect A
if (!file.exists("results/findeffects_TF_newparams.gz")){
  message("loading find.effects")
  find.effects <- fread("results/findeffects_all_newparams.gz")
  
  message("getting cases where A->B = T and B->A = F")
  find.effects_TF.1 <- find.effects[find.effects$`A->B`==T & find.effects$`B->A`==F,
                                    .(geneA, geneB, eqtl.A, eqtl.B, `A->B`, `B->A`)]
  find.effects_TF.2 <- rbind(find.effects_TF.1, 
                             find.effects[find.effects$`A->B`==F & find.effects$`B->A`==T & 
                                            var.exp.B > var.exp.lim, 
                                          .(geneA=geneB, geneB=geneA, eqtl.A=eqtl.B, 
                                            eqtl.B=eqtl.A, `A->B`=`B->A`, `B->A`=`A->B`)])
  find.effects_TF <- unique(find.effects_TF.2)
  
  message("saving to file")
  fwrite(find.effects_TF, "results/findeffects_TF_newparams.gz")

} else if (file.exists("results/findeffects_TF_newparams.gz")){
  message("loading find.effects_TF")
  find.effects_TF <- fread("results/findeffects_TF_newparams.gz")
}
## loading find.effects_TF

3.1.1 Plot number of times a gene pair appears

find.effects_TF.numpairs <- find.effects_TF[, .N, by=.(geneA, geneB)]

numpairs.table <- data.table(N = unique(find.effects_TF.numpairs$N), numpairs =NA)
for (i in 1:nrow(numpairs.table)){
  numpairs.table$numpairs[i] <- nrow(find.effects_TF.numpairs[N==numpairs.table$N[i]])
}

# plot the number of times a gene pair appears
bp <- barplot(numpairs.table[order(-numpairs)]$numpairs, 
              numpairs.table[order(-numpairs)]$N, names.arg=unique(find.effects_TF.numpairs[order(N)]$N), 
              width = 0.5, space=0.2, legend.text = F, ylim = c(0,max(numpairs.table$numpairs)+2500),
              main = "Number of times gene pairs appear \n (A->B = T and B->A = F)", xlab = "# times a gene pair appears", 
              ylab = "# of gene pairs")
text(bp,numpairs.table[order(-numpairs)]$numpairs, 
     labels=numpairs.table[order(-numpairs)]$numpairs, cex=1, pos=3)

3.1.2 Plot the number of times a gene-eqtl pair appears

3.1.3 Frequency of gene-eqtl pairs

find.effects_TF.geneeqtl.A <- find.effects_TF[, .N, by=.(geneA, eqtl.A)]
find.effects_TF.geneeqtl.A.plot <- find.effects_TF.geneeqtl.A %>% unite(gene_eqtlA, geneA, eqtl.A, sep = "__")
numpairs.table2 <- data.table(N = unique(find.effects_TF.geneeqtl.A.plot$N), numpairs =NA)
for (i in 1:nrow(numpairs.table2)){
  numpairs.table2$numpairs[i] <- nrow(find.effects_TF.geneeqtl.A[N==numpairs.table2$N[i]])
}
numpairs.table2 <- numpairs.table2[order(N)]

hist(find.effects_TF.geneeqtl.A.plot$N, main = "Frequency of gene-eqtl pairs", xlab = "# times gene-eqtl pairs appear",labels = T, ylim = c(0,780))

# the majority of gene-eqtl pairs appear between 0-100 times

3.1.4 Plot the network

Takes a very long time and you can’t really see anything so it’s not very worth it.
There are no subclusters, it’s just one bit network

plot_TF <- graph_from_edgelist(as.matrix(find.effects_TF[,.(geneA, geneB)]),directed=TRUE)
# components(plot_TF)

memb <- components(plot_TF)$membership
nodes <- data.table(id = names(memb),
                    group_id = memb)
nodes <- nodes[order(nodes$id), ]

if(length(unique(nodes$group_id))> 1){
  message(paste0("There are ", length(unique(nodes$group_id)), " subclusters!"))
} else {
  message("There are no subclusters")
}
## There are no subclusters
#plot(plot_TF, layout=layout_with_gem,edge.arrow.size=.5, vertex.label=NA)

From the 2884 genes with an eqtl in cis, only 741 (25.69%) of the genes have a causal effect.

4 GO analysis

In order to find the GO terms associated with my genes I used YeastMine (Balakrishnan et al., 2012) (https://yeastmine.yeastgenome.org/yeastmine/begin.do). Since I was not being able to do it in R, using the YeastMine API, I used python to run my queries. To be able to run the queries with python, first I needed to create an account and request an API key. Since you can generate python code from the website, I used it as a guide and added/ removed parameters to get what I needed.

To get an API key, go to https://yeastmine.yeastgenome.org/yeastmine/begin.do and create an account. Go to account details and create a new key. Save the key in a file called yeastmineAPI.txt

“genelist” is a list of the genes involved in the causality - Uploaded into yeastmine and saved in my area

  1. create gene list
if (!file.exists("data/genelist.txt")){
  genelist <- data.table(unique(c(find.effects_TF$geneA, find.effects_TF$geneB)))
  fwrite(genelist, "data/genelist.txt", col.names = F)
}

genelist <- fread("data/genelist.txt", header = F)
genes_in_find.effects <- data.table(unique(c(find.effects_TF$geneA, find.effects_TF$geneB)))
if (nrow(genelist) != nrow(genes_in_find.effects)){
  update_genelist <- T
  stop("YOU'LL NEED TO REUPLOAD THE GENELIST INTO YEASTMINE AND RERUN THE PYTHON CODE BELOW")
} else {
  update_genelist <- F
}
  1. upload list into yeastmine giving it the name genelist
  2. Continue once you have uploaded the genelist into yeastmine

Get table with geneID, gene symbol, gene name, GO identifier, GO term name, GO namespace and evidence code

# Sometimes breaks Rstudio, might be better to run it in a script/python console
import os.path
from os import path
os.chdir("/Users/Carolina/Documents/GitHub/DegreeProject/summarizing/")
if not path.isfile("results/genelistwithGOterm.txt"):
  # The following lines will be needed in every python script:
  from intermine.webservice import Service
  yeastmineAPItoken_file = open('data/yeastmineAPI.txt', 'r')
  yeastmineAPItoken = yeastmineAPItoken_file.readline().rstrip()
  service = Service("https://yeastmine.yeastgenome.org/yeastmine/service", token = yeastmineAPItoken)
  
  # Get a new query on the class (table) you will be querying:
  query = service.new_query("Gene")
  
  # The view specifies the output columns
  query.add_view(
      "secondaryIdentifier", "symbol", "name",
      "ontologyAnnotations.ontologyTerm.identifier",
      "ontologyAnnotations.ontologyTerm.name",
      "ontologyAnnotations.ontologyTerm.namespace",
      "ontologyAnnotations.evidence.code.code"
  )
  
  # You can edit the constraint values below
  query.add_constraint("Gene", "IN", "genelist", code = "A")
  
  terms = "gene", "symbol", "gene.name", "GO.identifier", "GO.term", "GO.namespace", "evidence"
  
  terms_query = ["secondaryIdentifier", "symbol", "name", "ontologyAnnotations.ontologyTerm.identifier", "ontologyAnnotations.ontologyTerm.name", "ontologyAnnotations.ontologyTerm.namespace", "ontologyAnnotations.evidence.code.code"]
  print("saving file")
  with open("results/genelistwithGOterm.txt", "w") as file:
    # write headers
    for term in terms[:-1]:
      file.write(term)
      file.write("\t")
    else:
      file.write(terms[-1])
    file.write("\n")
    #write content
    for row in query.rows():
      for t in terms_query[:-1]:
        if row[t] != None:
          file.write(str(row[t]))
          file.write("\t")
        else:
          file.write("NA")
          file.write("\t")
      if row[terms_query[-1]] != None:
        file.write(row[terms_query[-1]])
      else:
        file.write("NA")
      file.write("\n")
else:
  print("file exists")
## file exists

Load file generated with python - contains gene, gene symbol, gene name, GO code, GO term, GO namespace and evidence code

genes_GO.table <- fread("results/genelistwithGOterm.txt")
genes_GO.table <- unique(genes_GO.table)

Subset to have only the things we’ll need for the next analysis - GO terms related to biological processes and remove unecessary columns

genes_GO.bio <- unique(genes_GO.table[GO.namespace=="biological_process"])
genes_GO.bio.noevidence <- data.table(genes_GO.bio)
genes_GO.bio.noevidence[,evidence :=NULL]
genes_GO.bio.noevidence[,GO.namespace :=NULL]
genes_GO.bio.noevidence <- unique(genes_GO.bio.noevidence)

Run to get different measures

#count number of times each ontology term appears (number of diferent genes that have this GO term)
GOterms.count <- genes_GO.bio.noevidence[, .(description=unique(GO.term), count = .N), by = GO.identifier][order(-count)]

# find genes that have "transcription factor" in the GO term
GO_trans_factor <- unique(genes_GO.bio.noevidence[grepl("transcription factor",GO.term, fixed = F)])

# gene symbol + GO term + gene name
GO_transc_reg <- unique(genes_GO.bio.noevidence[(grepl(" transcription ",GO.term, fixed = F) & grepl(" regulation ",GO.term, fixed = F)) | grepl("transcription factor",GO.term, fixed = F)])

4.0.1 number of times each geneA and geneB point to another gene

numlinks_from_gene.A <- find.effects_TF[, .(geneB, count.A=.N), by=geneA]
numlinks_from_gene.B <- find.effects_TF[, .(count.B=.N), by=geneB]
numlinks_from_gene <- merge(numlinks_from_gene.A, numlinks_from_gene.B, by="geneB", all=T)
# save(numlinks_from_gene, file = "results/numlinks_from_gene.Rdata")
setcolorder(numlinks_from_gene, c("geneA", "geneB", "count.A", "count.B"))
head(numlinks_from_gene[order(-count.A, count.B)])
geneA geneB count.A count.B
YLR270W YDR236C 836 2
YLR270W YGL193C 836 2
YLR270W YJL008C 836 2
YLR270W YJL193W 836 3
YLR270W YJL143W 836 4
YLR270W YLR179C 836 4

4.0.2 number of times each geneA points to another gene + GO term

links_perGO_pergene <- merge(unique(genes_GO.bio.noevidence), 
                             unique(numlinks_from_gene[,.(geneA, count.A)]),
                             by.x="gene", by.y="geneA", all=T)
head(links_perGO_pergene[order(-count.A), .(gene, GO.identifier, GO.term, count.A)])
gene GO.identifier GO.term count.A
YLR270W GO:0000290 deadenylation-dependent decapping of nuclear-transcribed mRNA 836
YLR270W GO:0009267 cellular response to starvation 836
YLR270W GO:0031086 nuclear-transcribed mRNA catabolic process, deadenylation-independent decay 836
YLR270W GO:1901919 positive regulation of exoribonuclease activity 836
YLR264W GO:0000028 ribosomal small subunit assembly 806
YLR264W GO:0002181 cytoplasmic translation 806 
links_perGO <- links_perGO_pergene[,.(gocount=sum(count.A, na.rm = T)), by=c("GO.identifier", "GO.term")]

4.0.3 find the GOs that have transcription factor or transcription regulation in the name

links_perGO_transfactor <- links_perGO[(grepl(" transcription ",GO.term, fixed = F) & 
                                         grepl(" regulation ",GO.term, fixed = F)) | 
                                         grepl("transcription factor",GO.term, fixed = F)]
head(links_perGO_transfactor[order(-gocount)][,.(GO.term, gocount)])
GO.term gocount
positive regulation of transcription by RNA polymerase II 493
negative regulation of transcription by RNA polymerase II 433
negative regulation of transcription by transcription factor localization 109
positive regulation of transcription elongation from RNA polymerase II promoter 107
positive regulation of transcription initiation from RNA polymerase II promoter 86
positive regulation of transcription by RNA polymerase I 33

There are 65 different terms with “transcription” and “regulation” or “transcription factor” in their name

4.0.7 10 most represented GO categories

nms <- links_perGO[order(-gocount)]$GO.term[1:10]
mytable <- links_perGO[order(-gocount)]$gocount[1:10]
# labels with count
lbls <- paste( links_perGO[order(-gocount)]$GO.term[1:10], " -- ", mytable, sep="") 
pie(mytable, labels = nms,
    main = "Top 10 most represented GO categories \n (in causal genes)")
Most represented GO categories in the causal genes

Most represented GO categories in the causal genes

According to EBI, the GO term “biological process” is “any process that modulates the frequency, rate or extent of a biological process” it’s usually used when the actual function of the gene is not known.

High values might be because there are many genes associated with a certain term or because the genes that are associated with that term have many “arrows” going out (they affect many other genes)

5 GO Enrichment

I used GOstats, an R package (bioconductor), to test GO terms for over representation. I used both a classical hypergeometric test and a conditional hypergeometric test, which uses the relationships among GO terms to decorrelate the results

First I needed to define a few parameters:
* universe - all the genes in the dataset (can be involved in the causality or not) (num genes = 5720)
* interesting genes - causal genes (n=2658) or affected genes (n=2478)

Falcon & Gentleman (2007) the universe can be reduced by not using the genes that are not being expressed (in this case I would say not involved in the causality). Taking this into account, it would be interesting to perform the hypergeometric test using only the genes involved in the causality (genes that affect the expression of other genes and genes that are affected) as universe. Falcon & Gentleman (2007) also suggest removing genes that do not map to any GO term
I’m performing the hypergeometric test twice, once for the causal genes and once for the affected genes to see if there’s a different enrichment in both groups. It would be expected that the causal group would be enriched for genes involved in regulation.

From Falcon & Gentleman (2007)
“In the hypergeometric model, each term is treated as an independent classification. Each gene is classified according to whether or not it has been selected and whether or not it is annotated at a particular term. A hypergeometric probability is computed to assess whether the number of selected genes associated with the term is larger than expected.”

Performed new hypergeometric test with
* universe - all the genes involved in the causality (num genes = 2861)
* interesting genes - causal genes or affected genes

I checked if the resulting enrichment table was the same for both universes tested and it was.
I will continue by using the results from the second test, where the universe was comprised of genes involved in the causality.

5.1 Get enrichment for the “causal” genes and for the affected genes

5.1.1 Hypergeometric test

Causal - genesA
Affected - genesB

Test universe as all the genes in the dataset and universe as all the genes involved in the causality

if (!exists("genes_GO.bio")){
  genes_GO.table <- fread("results/genelistwithGOterm.txt")
  genes_GO.table <- unique(genes_GO.table)
  genes_GO.bio   <- unique(genes_GO.table[GO.namespace=="biological_process"])
}

# create geneset
goframeData <- unique(genes_GO.bio[,.(GO.identifier, evidence, gene)])
gs <- getgeneset(goframeData)


genesA <- unlist(unique(find.effects_TF[,geneA]))
genesB <- unlist(unique(find.effects_TF[,geneB]))

# universe is genes involved in causality
universe <- unique(c(genesA, genesB))


# get enrichment for genesA
res.geneA <- getenrichment(gs, universe = universe, interestinggenes = genesA)
res.geneA.dt <- data.table(summary(res.geneA))

# get enrichment for genesB
res.geneB <- getenrichment(gs, universe = universe, interestinggenes = genesB)
res.geneB.dt <- data.table(summary(res.geneB))


# universe is all the genes
universe.all <- names(phenotype[,2:ncol(phenotype)])

res.geneA.uniall <- getenrichment(gs, universe = universe.all, interestinggenes = genesA)
res.geneA.uniall.dt <- data.table(summary(res.geneA.uniall))

# get enrichment for genesB
res.geneB.uniall <- getenrichment(gs, universe = universe.all, interestinggenes = genesB)
res.geneB.uniall.dt <- data.table(summary(res.geneB.uniall))


# check if I got the same results using the universe = all genes and universe = genes involved in causality
if (!isTRUE(all.equal(res.geneA, res.geneA.uniall)) | isTRUE(all.equal(res.geneB, res.geneB.uniall))) {
  message("the hypergeometric test with the different universes didn't give the same results")
} else {
  message("The hypergeometric test for the different universes gave the same results")
}
## the hypergeometric test with the different universes didn't give the same results

5.1.1.1 To get the GO term graph for the geneA or geneB enrichment

(where universe is all the genes involved in the causality)

#save all the graphs to a pdf
if (!file.exists("results/figures/termgraph_A.pdf") | update_genelist){
  
  termgrA <- termGraphs(res.geneA, use.terms = T, pvalue = 0.05)
  pdf(file = "results/figures/termgraph_A.pdf", onefile = T)
  for (i in 1:length(termgrA)){
    plotGOTermGraph(termgrA[[i]], r = res.geneA, add.counts = T, 
                    node.colors=c(sig="green", not="white"), max.nchar=30)
  }
  dev.off()
  message("saved the pdf with GO term graphs for the causal genes")
  
} else {
  message("pdf with graphs for the causal genes already exists")
}
## pdf with graphs for the causal genes already exists
if (!file.exists("results/figures/termgraph_B.pdf") | update_genelist){
  
  termgrB <- termGraphs(res.geneB, use.terms = T, pvalue = 0.05)
  
  pdf(file = "results/figures/termgraph_B.pdf", onefile = T)
  for (i in 1:length(termgrB)){
    plotGOTermGraph(termgrB[[i]], r = res.geneB, add.counts = T, 
                    node.colors=c(sig="green", not="white"), max.nchar=30)
  }
  dev.off()
  message("saved the pdf with GO term graphs for the affected genes")
  
} else {
  message("pdf with graphs for the affected genes already exists")
}
## pdf with graphs for the affected genes already exists

5.1.2 Conditional Hypergeometric test

if (!exists("genes_GO.bio")){
  genes_GO.table <- fread("results/genelistwithGOterm.txt")
  genes_GO.table <- unique(genes_GO.table)
  genes_GO.bio   <- unique(genes_GO.table[GO.namespace=="biological_process"])
}

# create geneset
goframeData <- unique(genes_GO.bio[,.(GO.identifier, evidence, gene)])
gs <- getgeneset(goframeData)


genesA <- unlist(unique(find.effects_TF[,geneA]))
genesB <- unlist(unique(find.effects_TF[,geneB]))

# universe is genes involved in causality
universe <- unique(c(genesA, genesB))

# get enrichment
hgCondA <- getenrichment(gs, universe = universe, interestinggenes = genesA, cond = T)
hgCondA.dt <- data.table(summary(hgCondA))

hgCondB <- getenrichment(gs, universe = universe, interestinggenes = genesB, cond = T)
hgCondB.dt <- data.table(summary(hgCondB))

# causal genes
hgCondA
## Gene to GO BP Conditional test for over-representation 
## 3062 GO BP ids tested (115 have p < 0.05)
## Selected gene set size: 740 
##     Gene universe size: 2433 
##     Annotation package: Based on a GeneSetCollection Object
# affected genes
hgCondB
## Gene to GO BP Conditional test for over-representation 
## 4305 GO BP ids tested (25 have p < 0.05)
## Selected gene set size: 2277 
##     Gene universe size: 2433 
##     Annotation package: Based on a GeneSetCollection Object
hgCondA.dt
GOBPID Pvalue OddsRatio ExpCount Count Size Term
GO:0015074 0.0000033 6.690476 8.2120838 20 27 DNA integration
GO:2000112 0.0000294 1.883809 58.2262997 84 196 regulation of cellular macromolecule biosynthetic process
GO:0009889 0.0000479 1.825424 61.4006143 87 207 regulation of biosynthetic process
GO:0031328 0.0000545 1.831857 59.9177970 85 197 positive regulation of cellular biosynthetic process
GO:0032774 0.0000606 1.653361 91.2453761 121 300 RNA biosynthetic process
GO:0080090 0.0000759 1.669385 84.6262093 113 283 regulation of primary metabolic process
GO:0006351 0.0000839 1.641275 90.0287711 119 296 transcription, DNA-templated
GO:0010557 0.0000925 1.810992 58.0928894 82 191 positive regulation of macromolecule biosynthetic process
GO:0010628 0.0001103 1.803870 57.4845869 81 189 positive regulation of gene expression
GO:0006278 0.0002131 3.427669 11.2535964 22 37 RNA-dependent DNA biosynthetic process
GO:0018130 0.0002211 1.473873 142.6469379 175 469 heterocycle biosynthetic process
GO:0006310 0.0002433 2.525737 19.3488372 33 64 DNA recombination
GO:0019438 0.0002936 1.465007 140.5178792 172 462 aromatic compound biosynthetic process
GO:0031323 0.0002937 1.617189 78.9813167 104 265 regulation of cellular metabolic process
GO:0050789 0.0003480 1.506967 115.4398321 144 391 regulation of biological process
GO:1901362 0.0005162 1.426117 152.0756268 183 500 organic cyclic compound biosynthetic process
GO:0051254 0.0006350 1.717006 52.3140156 72 172 positive regulation of RNA metabolic process
GO:0006449 0.0007805 Inf 1.8249075 6 6 regulation of translational termination
GO:0045944 0.0009824 1.751615 44.4060830 62 146 positive regulation of transcription by RNA polymerase II
GO:0006508 0.0009953 1.625763 60.8302507 81 200 proteolysis
GO:1903508 0.0010438 1.691951 50.4891081 69 166 positive regulation of nucleic acid-templated transcription
GO:0051173 0.0010664 1.619066 60.9272954 81 202 positive regulation of nitrogen compound metabolic process
GO:0044271 0.0011298 1.354018 199.5232224 231 656 cellular nitrogen compound biosynthetic process
GO:0048522 0.0015723 1.529220 74.9325127 96 249 positive regulation of cellular process
GO:0009890 0.0015979 1.686259 46.8392931 64 154 negative regulation of biosynthetic process
GO:0032197 0.0017845 2.717895 11.8618989 21 39 transposition, RNA-mediated
GO:0006139 0.0019659 1.314026 238.7587341 270 785 nucleobase-containing compound metabolic process
GO:0090305 0.0020440 1.898887 28.5902178 42 94 nucleic acid phosphodiester bond hydrolysis
GO:2000113 0.0020722 1.690126 43.7977805 60 144 negative regulation of cellular macromolecule biosynthetic process
GO:0044260 0.0022341 1.322285 229.2397661 259 784 cellular macromolecule metabolic process
GO:1903507 0.0026994 1.742159 36.4981504 51 120 negative regulation of nucleic acid-templated transcription
GO:0006974 0.0030499 1.698192 39.2355117 54 129 cellular response to DNA damage stimulus
GO:0007059 0.0030749 1.990156 22.5071928 34 74 chromosome segregation
GO:0090502 0.0032152 2.331006 14.5992602 24 48 RNA phosphodiester bond hydrolysis, endonucleolytic
GO:0031929 0.0035390 4.242570 5.1705713 11 17 TOR signaling
GO:1903047 0.0039370 1.705524 36.1939992 50 119 mitotic cell cycle process
GO:0007165 0.0044264 1.609291 45.0143855 60 148 signal transduction
GO:0031146 0.0046075 8.074352 2.7373613 7 9 SCF-dependent proteasomal ubiquitin-dependent protein catabolic process
GO:0071704 0.0051567 1.274064 472.6510481 501 1554 organic substance metabolic process
GO:1903008 0.0058136 2.630337 9.7328401 17 32 organelle disassembly
GO:0051253 0.0060545 1.642643 37.7147554 51 124 negative regulation of RNA metabolic process
GO:0016070 0.0061015 1.452213 70.5594901 88 243 RNA metabolic process
GO:0043624 0.0079486 3.009491 6.9954788 13 23 cellular protein complex disassembly
GO:0043007 0.0085095 Inf 1.2166050 4 4 maintenance of rDNA
GO:0045143 0.0089690 4.156498 4.2581176 9 14 homologous chromosome segregation
GO:0140013 0.0091864 2.130803 13.9909577 22 46 meiotic nuclear division
GO:0006950 0.0093778 1.353247 100.0657624 119 329 response to stress
GO:0016042 0.0098089 2.211640 12.4702014 20 41 lipid catabolic process
GO:0034250 0.0103783 2.701332 7.9079326 14 26 positive regulation of cellular amide metabolic process
GO:0006817 0.0113429 5.379718 3.0415125 7 10 phosphate ion transport
GO:0006928 0.0113429 5.379718 3.0415125 7 10 movement of cell or subcellular component
GO:0051171 0.0114562 2.046312 14.1465433 22 49 regulation of nitrogen compound metabolic process
GO:0046686 0.0115678 11.510204 1.8249075 5 6 response to cadmium ion
GO:0000122 0.0120761 1.776702 22.5071928 32 74 negative regulation of transcription by RNA polymerase II
GO:0000819 0.0124401 2.044345 14.2951089 22 47 sister chromatid segregation
GO:0010608 0.0127158 1.749293 23.4196465 33 77 posttranscriptional regulation of gene expression
GO:0051716 0.0131314 1.350248 89.8730853 107 302 cellular response to stimulus
GO:0000086 0.0137669 3.299413 5.1705713 10 17 G2/M transition of mitotic cell cycle
GO:0031324 0.0150198 1.433615 56.2679819 70 185 negative regulation of cellular metabolic process
GO:0001302 0.0153214 2.774176 6.6913276 12 22 replicative cell aging
GO:0048519 0.0160641 1.343835 85.7706535 102 282 negative regulation of biological process
GO:1903506 0.0165833 2.031746 12.9411765 20 44 regulation of nucleic acid-templated transcription
GO:0007049 0.0184123 1.342701 81.5125360 97 268 cell cycle
GO:0010629 0.0194304 1.423026 53.2264694 66 175 negative regulation of gene expression
GO:0006357 0.0203374 2.065833 11.5498008 18 39 regulation of transcription by RNA polymerase II
GO:0051172 0.0222357 1.405315 54.4430744 67 179 negative regulation of nitrogen compound metabolic process
GO:0040008 0.0226889 2.885274 5.4747226 10 18 regulation of growth
GO:0032270 0.0230816 1.742987 19.1615290 27 63 positive regulation of cellular protein metabolic process
GO:0006448 0.0230875 4.032401 3.3456638 7 11 regulation of translational elongation
GO:1903432 0.0230875 4.032401 3.3456638 7 11 regulation of TORC1 signaling
GO:0033043 0.0233562 1.480462 39.2355117 50 129 regulation of organelle organization
GO:0071824 0.0241908 1.712744 20.0739827 28 66 protein-DNA complex subunit organization
GO:0051246 0.0249559 1.420677 48.3600493 60 159 regulation of protein metabolic process
GO:0006511 0.0270639 1.553656 28.8943691 38 95 ubiquitin-dependent protein catabolic process
GO:0007568 0.0278380 2.310867 7.9079326 13 26 aging
GO:0006384 0.0280570 Inf 0.9124538 3 3 transcription initiation from RNA polymerase III promoter
GO:0051446 0.0280570 Inf 0.9124538 3 3 positive regulation of meiotic cell cycle
GO:0071168 0.0280570 Inf 0.9124538 3 3 protein localization to chromatin
GO:0051228 0.0280570 Inf 0.9124538 3 3 mitotic spindle disassembly
GO:0120174 0.0280570 Inf 0.9124538 3 3 stress-induced homeostatically regulated protein degradation pathway
GO:1903711 0.0280570 Inf 0.9124538 3 3 spermidine transmembrane transport
GO:0002949 0.0280570 Inf 0.9124538 3 3 tRNA threonylcarbamoyladenosine modification
GO:0005980 0.0280570 Inf 0.9124538 3 3 glycogen catabolic process
GO:0006333 0.0286217 2.539506 6.3871763 11 21 chromatin assembly or disassembly
GO:0009059 0.0286478 1.299873 87.4954955 102 304 macromolecule biosynthetic process
GO:0051252 0.0295680 1.827077 14.4104545 21 49 regulation of RNA metabolic process
GO:0060237 0.0303689 5.751701 2.1290588 5 7 regulation of fungal-type cell wall organization
GO:0030705 0.0303689 5.751701 2.1290588 5 7 cytoskeleton-dependent intracellular transport
GO:0042908 0.0303689 5.751701 2.1290588 5 7 xenobiotic transport
GO:0046015 0.0303689 5.751701 2.1290588 5 7 regulation of transcription by glucose
GO:2000104 0.0322337 9.195652 1.5207563 4 5 negative regulation of DNA-dependent DNA replication
GO:0010603 0.0322337 9.195652 1.5207563 4 5 regulation of cytoplasmic mRNA processing body assembly
GO:0045903 0.0322337 9.195652 1.5207563 4 5 positive regulation of translational fidelity
GO:0000076 0.0322337 9.195652 1.5207563 4 5 DNA replication checkpoint
GO:0015828 0.0322337 9.195652 1.5207563 4 5 tyrosine transport
GO:0016567 0.0327043 1.706088 17.9449240 25 59 protein ubiquitination
GO:0009057 0.0330338 1.360520 56.3147460 68 186 macromolecule catabolic process
GO:0051276 0.0333557 1.379491 50.8420168 62 169 chromosome organization
GO:0043632 0.0335749 1.492959 31.9358816 41 105 modification-dependent macromolecule catabolic process
GO:0070192 0.0337030 3.072860 4.2581176 8 14 chromosome organization involved in meiotic cell cycle
GO:0043618 0.0391539 2.144527 8.2120838 13 27 regulation of transcription from RNA polymerase II promoter in response to stress
GO:0006855 0.0391539 2.144527 8.2120838 13 27 drug transmembrane transport
GO:0051130 0.0396372 1.565493 23.4196465 31 77 positive regulation of cellular component organization
GO:0048869 0.0402661 1.444229 34.9773942 44 115 cellular developmental process
GO:0035601 0.0411058 3.224011 3.6498150 7 12 protein deacylation
GO:0000272 0.0411058 3.224011 3.6498150 7 12 polysaccharide catabolic process
GO:0044089 0.0423795 1.736787 14.9034114 21 49 positive regulation of cellular component biogenesis
GO:1905268 0.0432545 2.593194 5.1705713 9 17 negative regulation of chromatin organization
GO:0008608 0.0432545 2.593194 5.1705713 9 17 attachment of spindle microtubules to kinetochore
GO:0006259 0.0467712 1.419733 35.2630231 44 119 DNA metabolic process
GO:0000226 0.0479839 2.130177 7.6037813 12 25 microtubule cytoskeleton organization
GO:0000725 0.0479839 2.130177 7.6037813 12 25 recombinational repair
GO:0006665 0.0479839 2.130177 7.6037813 12 25 sphingolipid metabolic process
GO:0010498 0.0493973 1.609363 18.5532265 25 61 proteasomal protein catabolic process
GO:0048285 0.0495738 1.490192 26.4611591 34 87 organelle fission 
hgCondB.dt
GOBPID Pvalue OddsRatio ExpCount Count Size Term
GO:0055086 0.0005735 10.464135 135.70284 144 145 nucleobase-containing small molecule metabolic process
GO:0044283 0.0009677 4.643028 180.62515 190 193 small molecule biosynthetic process
GO:0055114 0.0010395 3.921512 203.08631 213 217 oxidation-reduction process
GO:0009117 0.0014825 9.308659 121.66461 129 130 nucleotide metabolic process
GO:0051186 0.0017890 9.079498 118.85697 126 127 cofactor metabolic process
GO:0032787 0.0032288 Inf 79.54994 85 85 monocarboxylic acid metabolic process
GO:0019693 0.0032288 Inf 79.54994 85 85 ribose phosphate metabolic process
GO:1901293 0.0042496 Inf 75.80641 81 81 nucleoside phosphate biosynthetic process
GO:0046394 0.0042567 8.018476 105.75462 112 113 carboxylic acid biosynthetic process
GO:0072521 0.0052201 Inf 72.99877 78 78 purine-containing compound metabolic process
GO:0043436 0.0066456 2.673797 211.50925 220 226 oxoacid metabolic process
GO:1901566 0.0093448 1.815680 424.89026 436 454 organonitrogen compound biosynthetic process
GO:0017144 0.0096272 4.434742 117.92109 124 126 drug metabolic process
GO:0009123 0.0118536 Inf 61.76819 66 66 nucleoside monophosphate metabolic process
GO:0009150 0.0126896 Inf 60.83231 65 65 purine ribonucleotide metabolic process
GO:1901607 0.0135841 Inf 59.89642 64 64 alpha-amino acid biosynthetic process
GO:0090150 0.0268015 Inf 50.53761 54 54 establishment of protein localization to membrane
GO:0006733 0.0351440 Inf 46.79408 50 50 oxidoreduction coenzyme metabolic process
GO:0009260 0.0351440 Inf 46.79408 50 50 ribonucleotide biosynthetic process
GO:0006839 0.0351440 Inf 46.79408 50 50 mitochondrial transport
GO:0009167 0.0376047 Inf 45.85820 49 49 purine ribonucleoside monophosphate metabolic process
GO:0019637 0.0385480 2.823784 110.16014 115 118 organophosphate metabolic process
GO:0045333 0.0402366 Inf 44.92232 48 48 cellular respiration
GO:0006790 0.0453126 5.061224 68.31936 72 73 sulfur compound metabolic process
GO:0009199 0.0460616 Inf 43.05055 46 46 ribonucleoside triphosphate metabolic process

to filter the results, the following parameters can be added to the “summary()” call:
Optional arguments pvalue and categorySize allow specification of maximum p-value and minimum categorySize

5.1.2.1 Other analysis

if (exists("res.geneA") & exists("hgCondA") & exists("res.geneB") & exists("hgCondB")){
  # GO terms that are marked significant by the standard hypergeo test, but not by the conditional test
  stdIdsA = sigCategories(res.geneA)
  condIdsA = sigCategories(hgCondA)
  # num of GO terms that were not significant with the conditional hypergeo test
  # print(length(setdiff(stdIdsA, condIdsA)))
  
  stdIdsB = sigCategories(res.geneB)
  condIdsB = sigCategories(hgCondB)
  # num of GO terms that were not significant with the conditional hypergeo test
  # print(length(setdiff(stdIdsB, condIdsB)))
  
  # terms that are enriched in the hypergeo test but not on the conditional
  goterms_notin_condA <- res.geneA.dt[GOBPID %in% setdiff(stdIdsA, condIdsA)]
  goterms_notin_condB <- res.geneB.dt[GOBPID %in% setdiff(stdIdsB, condIdsB)]
  
  # create HTML reports (tables with enrichment) where the GO terms have links that
  # take you to a page where you can learn more about them
  htmlReport(hgCondA, file="results/hgCondA_htmlreport.html")
  htmlReport(hgCondB, file="results/hgCondB_htmlreport.html")
} else {
  message("Please perform the conditional and the 'normal' hypergeometric test")
}

Number of GO terms that were not significant with the conditional hypergeo test:
* for the causal genes - 91
* for the affected genes - 18

set of genes Hypergeo Conditional hypergeo
causal 206 115
affected 43 25

5.1.2.2 GO Enrichment Heatmap (-log10(pval))

if (exists("hgCondA.dt") & exists("hgCondB.dt")){
  tocombine.A <- data.table(-log10(hgCondA.dt$Pvalue), hgCondA.dt$Term)
  tocombine.B <- data.table(-log10(hgCondB.dt$Pvalue), hgCondB.dt$Term)
  combined <- merge(tocombine.A, tocombine.B, by="V2", all=T)
  colnames(combined) <- c("term", "Causal", "Affected")
  
  # Causal <- combined$Causal
  # Affected <- combined$Affected

  if (!file.exists("results/figures/heatmap_enrichmentpvals.pdf") | update_genelist){
    
    pdf("results/figures/heatmap_enrichmentpvals.pdf")
    combined[is.na(Causal)]$Causal <- 0
    combined[is.na(Affected)]$Affected <- 0
    plot_enrichment_heatmap(combined)
    dev.off()
    plot_enrichment_heatmap(combined)
  } else {
    combined[is.na(Causal)]$Causal <- 0
    combined[is.na(Affected)]$Affected <- 0
    plot_enrichment_heatmap(combined)
  }
} else {
  stop("Please perform the conditional hypergeometric test")
}

5.1.2.3 GO Enrichment Heatmap (-log10(pval)) subsetting by p-val

if (exists("hgCondA.dt") & exists("hgCondB.dt")){
  tocombine.A <- data.table(-log10(hgCondA.dt$Pvalue), hgCondA.dt$Term)
  tocombine.B <- data.table(-log10(hgCondB.dt$Pvalue), hgCondB.dt$Term)
  combined <- merge(tocombine.A, tocombine.B, by="V2", all=T)
  colnames(combined) <- c("term", "Causal", "Affected")
  
  # Causal <- combined$Causal
  # Affected <- combined$Affected

  if (!file.exists("results/figures/heatmap_enrichmentpvals_0.01pval.pdf") | update_genelist){
    
    pdf("results/figures/heatmap_enrichmentpvals_0.01pval.pdf")
    combined[is.na(Causal)]$Causal <- 0
    combined[is.na(Affected)]$Affected <- 0
    plot_enrichment_heatmap(combined[Causal > -log10(0.01) | Affected > -log10(0.01)])
    dev.off()
    plot_enrichment_heatmap(combined[Causal > -log10(0.01) | Affected > -log10(0.01)])
  } else {
    combined[is.na(Causal)]$Causal <- 0
    combined[is.na(Affected)]$Affected <- 0
    plot_enrichment_heatmap(combined[Causal > -log10(0.01) | Affected > -log10(0.01)])
  }
} else {
  stop("Please perform the conditional hypergeometric test")
}

5.2 Using a subset of the causal and the affected genes

if (!exists("find.effects_TF")){
  find.effects_TF <- fread("results/findeffects_TF_newparams.gz")
}
numlinks_from_geneA <- unique(find.effects_TF[, .(geneA, geneB)])[, .(l_out = .N), by = geneA]
numlinks_from_geneB <- unique(find.effects_TF[, .(geneA, geneB)])[, .(l_in = .N), by = geneB]

# table with how many links go in and out of a gene
genes <-data.table(genes = unique(c(find.effects_TF$geneA, find.effects_TF$geneB)))
merge_lin <-merge( genes, numlinks_from_geneA, by. = "genes", by.y = "geneA", all.x = T)
genes_nlinks <-merge( merge_lin, numlinks_from_geneB, by.x = "genes", by.y = "geneB", all.x = T)
# genes_nlinks[order(-l_out, l_in)]

# scatterplot of link distribution
plot(genes_nlinks[, .(l_out, l_in)], main = "How many links each gene has going in or out", pch =".")

plot(genes_nlinks[, .(l_out, l_in)], xlim = c(0, 200), pch = ".")
abline(v = 20)

There are 1 genes that have more links going in than out in the causal genes.

5.2.1 Conditional hypergeometric test with new subset of causal and affected genes

# creating the sets of genes to use
# defined causalgenes as the genes that have more than 20 links going out
causalgenes <- genes_nlinks[l_out > 20]
# defined affectedgenes as the genes that have no links going out
affectedgenes <- genes_nlinks[is.na(l_out) & !is.na(l_in)]

# the "interesting genes" - either genesA (the causal ones) or genesB (on the receiving end)
# the "universe" - in this case, only the genes that are involved in the causality
genesA <- causalgenes$genes
genesB <- affectedgenes$genes

universe <- unlist(unique(c(find.effects_TF[, geneA], find.effects_TF[, geneB])))

# create geneset
gs <- getgeneset(goframeData)

# get enrichment for genesA
hgCondA.sub <- getenrichment(gs, universe = universe, interestinggenes = genesA, cond = T)
hgCondA.sub.dt <- data.table(summary(hgCondA.sub))

hgCondB.sub <- getenrichment(gs, universe = universe, interestinggenes = genesB, cond = T)
hgCondB.sub.dt <- data.table(summary(hgCondB.sub))


# table with all enriched GO terms found for both the causal and the affected genes with the corresponding enrichment p-val
tocombine.A <- data.table(-log10(hgCondA.dt$Pvalue), hgCondA.dt$Term)
tocombine.B <- data.table(-log10(hgCondB.dt$Pvalue), hgCondB.dt$Term)
combined <- merge(tocombine.A, tocombine.B, by = "V2", all = T)
colnames(combined) <- c("term", "Causal", "Affected")
combined[is.na(Causal)]$Causal <- 0
combined[is.na(Affected)]$Affected <- 0

if (!file.exists("results/figures/heatmap_enrichmentpvals_geneslinks_c20.pdf") | update_genelist){
  # plot GO enrichment heatmap
  pdf("results/figures/heatmap_enrichmentpvals_geneslinks_c20.pdf")
  plot_enrichment_heatmap(combined)
  dev.off()
  
  plot_enrichment_heatmap(combined)
} else {
  plot_enrichment_heatmap(combined)
}

5.2.1.1 Plot of the p-values of enrichment of all terms for all genes

(subset of causal and affected genes)

hgCondA.nopvallim <- getenrichment(gs, universe = universe, interestinggenes = genesA, cond = T, pval = 1)
hgCondA.nopvallim.dt <- data.table(summary(hgCondA.nopvallim))

hgCondB.nopvallim <- getenrichment(gs, universe = universe, interestinggenes = genesB, cond = T, pval = 1)
hgCondB.nopvallim.dt <- data.table(summary(hgCondB.nopvallim))

pvalsmerge <- merge(hgCondA.nopvallim.dt[,.(GOBPID, Term, Pvalue)], hgCondB.nopvallim.dt[,.(GOBPID, Term, Pvalue)], by=c("GOBPID", "Term"), all=T)
plot(-log10(pvalsmerge$Pvalue.x), -log10(pvalsmerge$Pvalue.y), pch=".")

6 Plot similar to the one from the paper

Albert FW, Bloom JS, Siegel J, Day L, Kruglyak L. 2018. Genetics of trans-regulatory variation in gene expression. eLife 7: 1–39.

6.1 get gene position

# Sometimes breaks Rstudio, might be better to run it in a script/python console
import os.path
from os import path
os.chdir("/Users/Carolina/Documents/GitHub/DegreeProject/summarizing/")

if not path.isfile("results/gene_pos.gz"):
  # The line below will be needed if you are running this script with python 2.
  #from __future__ import print_function
  # The following two lines will be needed in every python script:
  from intermine.webservice import Service
  yeastmineAPItoken_file = open('/Users/Carolina/Documents/GitHub/DegreeProject/code/2020-02-05/yeastmineAPI.txt', 'r')
  yeastmineAPItoken = yeastmineAPItoken_file.readline().rstrip()
  service = Service("https://yeastmine.yeastgenome.org/yeastmine/service", token = yeastmineAPItoken)
  # Get a new query on the class (table) you will be querying:
  query = service.new_query("Gene")
  # The view specifies the output columns
  query.add_view(
      "secondaryIdentifier", "chromosomeLocation.strand",
      "chromosomeLocation.start", "chromosomeLocation.end", "chromosome.primaryIdentifier"
  )
  # You can edit the constraint values below
  query.add_constraint("Gene", "IN", "allgenes", code = "A")
  terms = "gene", "chr.strand", "chr.start", "chr.end", "chr.id"
  terms_query = ["secondaryIdentifier", "chromosomeLocation.strand", "chromosomeLocation.start", "chromosomeLocation.end", "chromosome.primaryIdentifier"]
  with open("results/gene_pos.gz", "w") as file:
    for term in terms[:-1]:
      file.write(term)
      file.write ("\t")
    else:
      file.write(terms[-1])
    file.write('\n')
    for row in query.rows():
      for t in terms_query[:-1]:
        if row[t] != None:
          file.write(str(row[t]))
          file.write("\t")
        else:
          file.write("NA")
          file.write("\t")
      if row[terms_query[-1]] != None:
        file.write(row[terms_query[-1]])
      else:
        file.write("NA")
      file.write("\n")

6.1.1 Plot affected genes x causal genes

genepos <- fread("results/gene_pos.gz")
if (!exists("find.effects_TF")){
  find.effects_TF <- fread("results/findeffects_TF_newparams.gz")
}

# Add chomosome and start position to each gene
causal.pos.A <- merge(find.effects_TF, genepos, by.x="geneA", by.y="gene", all.x=T)
colnames(causal.pos.A) <- c("geneA", "geneB", "eqtl.A", "eqtl.B", "A->B", "B->A", "strand.A", "start.A","end.A", "chr.A")
causal.pos.B <- merge(causal.pos.A, genepos, by.x="geneB", by.y="gene", all.x=T)


# Keep olnly columns with genes, start positions and chromosomes
gene.location <- unique(causal.pos.B[,.(geneA, geneB, start.A,end.A, chr.A, chr.start,chr.end, chr.id, strand.A, chr.strand)])
colnames(gene.location) <- c("geneA", "geneB", "start.A","end.A", "chr.A", "start.B","end.B", "chr.B", "strand.A", "strand.B")

## transform chromosome ids into numbers
# remove "chr" part of the chromosome name
gene.location$chr.A <-gsub('chr', '', gene.location$chr.A)
gene.location$chr.B <-gsub('chr', '', gene.location$chr.B)

# convert roman chromosome numbers to numbers
gene.location$chr.A <- as.numeric(as.roman(gene.location$chr.A))
gene.location$chr.B <- as.numeric(as.roman(gene.location$chr.B))

# order values
gene.location.order <- gene.location[order(chr.A, start.A, chr.B, start.B)]


# organize coordinates so that they are ordered by chromosome
# vector of chromosomes
vchr <- 1:16
# how much space will be separating chromosomes
separator <- 1e5

coordinates_plot.start <- sort_by_chr(vchr = vchr, gene.location.order, separator = separator)
coordinates_plot.end <- sort_by_chr(vchr = vchr, gene.location.order, separator = separator, coordA = 4, coordB = 7 )

coordinates_plot <- merge(coordinates_plot.start[,.(geneA, geneB, start.A, chr.A, start.B, chr.B, strand.A, strand.B)], 
                          coordinates_plot.end[,.(geneA, geneB, end.A, end.B)], 
                          by=c("geneA", "geneB"))
setcolorder(coordinates_plot, c("geneA","geneB", "start.A", "end.A", "chr.A", "start.B", "end.B", "chr.B"))

plot_sorted_coordinates(coordinates_plot, separator = separator)

6.1.1.1 color by correlation value

if (!exists("find.effects")){
  find.effects <- fread("results/findeffects_all_newparams.gz")
}
#Create a function to generate a continuous color palette
rbPal <- colorRampPalette(c('blue','red'))


coordinates_plot_cor <- merge(coordinates_plot, find.effects[,.(geneA, geneB, cor)], by=c("geneA", "geneB"), all.x=T)
coordinates_plot_cor$cor <- abs(coordinates_plot_cor$cor)
coordinates_plot_cor <- coordinates_plot_cor[order(cor)]
coordinates_plot_cor$col <- rbPal(100)[as.numeric(cut(coordinates_plot_cor$cor,breaks = 100))]

#save(coordinates_plot_cor, file="results/coordinates_plot_cor.Rdata")
plot_sorted_coordinates(coordinates_plot_cor, separator = separator, col = coordinates_plot_cor$col)
gradientLegend(format(round(coordinates_plot_cor$cor, 3), nsmall = 3), rbPal(100), inside=F, side=3)
Pairs of genes where the gene on the x-axis if affecting the gene on the y-axis. Colored by correlation value and sorted by chromosome and position

Pairs of genes where the gene on the x-axis if affecting the gene on the y-axis. Colored by correlation value and sorted by chromosome and position

6.1.2 Which genes in the paper are also in my results

Table with the number of times a gene form the paper is the causal or the affected gene

# genes in hotspots referred to in the text
genes_in_text <- c("GPA1", "ERC1", "STB5", "HAP1", "KRE33", "MKT1", "IRA2")

# genes from the list above that are in my dataset 
my_genes_in_text <- unique(genes_GO.bio[symbol %in% genes_in_text, .(gene, symbol)])

# keep causality results where either geneA or geneB are in the list above
my_genes_in_text.effects <- find.effects_TF[geneA %in% my_genes_in_text$gene | 
                                            geneB %in% my_genes_in_text$gene]
# my_genes_in_text.effects <- find.effects_TF[geneA %in% my_genes_in_text$gene]

# replace my geneIds with the gene symbol (like what they have in the paper)
for (rown in 1:nrow(my_genes_in_text)){
  my_genes_in_text.effects[geneA == my_genes_in_text[rown]$gene]$geneA <- my_genes_in_text[rown]$symbol
  my_genes_in_text.effects[geneB == my_genes_in_text[rown]$gene]$geneB <- my_genes_in_text[rown]$symbol
}
# my_genes_in_text.effects[geneA %in% my_genes_in_text$symbol,.N, by=geneA]
# my_genes_in_text.effects[geneB %in% my_genes_in_text$symbol,.N, by=geneB]
# number of times a gene is the causal one or the affected one
mygenes_paper.count <- merge(my_genes_in_text.effects[geneA %in% my_genes_in_text$symbol,.N, by=geneA], 
      my_genes_in_text.effects[geneB %in% my_genes_in_text$symbol,.N, by=geneB],
      by.x="geneA", by.y="geneB", all=T)
setnames(mygenes_paper.count, c("gene", "causal", "affected"))
mygenes_paper.count[order(-causal)]
gene causal affected
GPA1 68 11
ERC1 27 8
STB5 7 NA
KRE33 NA 6

7 Focus on causal gene 12

# coordinates_plot_cor[chr.A==12]
plot_sorted_coordinates(coordinates_plot_cor[chr.A==12], separator = separator, col = coordinates_plot_cor[chr.A==12]$col)

7.1 Focus on the group of vertical bands

Chromosome 3 seems to mostly be affected by the genes in the vertical bands so I’m going to look closer at chromosome 3

# the first gene of chromosome 12 that affects chromosome 3
lower_limit <- coordinates_plot_cor[chr.A==12 & chr.B==3][order(start.A)][1]$start.A
# the last gene on chromosome 12 that affects chromosome 3 and that's in the band (there's an extra gene further away from the band)
top_limit <- coordinates_plot_cor[chr.A==12 & chr.B==3][order(start.A)][nrow(coordinates_plot_cor[chr.A==12 & chr.B==3])-1]$start.A

# plot chromosome 12 - the green lines are the limits of the group of vertical bands
plot_sorted_coordinates(coordinates_plot_cor[chr.A==12], separator = separator, col = coordinates_plot_cor[chr.A==12]$col)
abline(v = c(lower_limit-1500, top_limit+1500), col="green")

there are 30 genes between the two green bands

7.1.1 get gene names for genes on chromosome 12

if (!exists("genes_GO.bio")){
  genes_GO.table <- fread("results/genelistwithGOterm.txt")
  genes_GO.table <- unique(genes_GO.table)
  genes_GO.bio   <- unique(genes_GO.table[GO.namespace=="biological_process"])
}
genesA_start_order <- unique(coordinates_plot_cor[chr.A == 12 & chr.B == 3][order(start.A)][, .(geneA, start.A)])
chr12_genenames_chr3 <- merge(genesA_start_order, unique(genes_GO.bio[,.(gene, symbol, gene.name, GO.term)]), by.x="geneA", by.y="gene")
chr12_genenames <- merge(unique(coordinates_plot_cor[chr.A == 12][order(start.A)][, .(geneA, start.A)]), unique(genes_GO.bio[,.(gene, symbol, gene.name, GO.term)]), by.x="geneA", by.y="gene")

7.1.2 Look at the first group

# genes that are affecting chromosome 3 + positions
band1.left <- min(genesA_start_order$start.A)
band1.right <- 8323026
plot_sorted_coordinates(
  coordinates_plot_cor[chr.A == 12],
  separator = separator,
  col = coordinates_plot_cor[chr.A==12]$col,
  xlim = c(band1.left, top_limit)
)
abline(v=c(band1.left-1500, band1.right+1500), col="green")

There are 31 between the green lines

# genes that are affecting chromosome 3 + positions
plot_sorted_coordinates(
  coordinates_plot_cor[chr.A == 12],
  separator = separator,
  col = coordinates_plot_cor[chr.A==12]$col,
  xlim=c(band1.left, band1.right)
)
abline(v=c(band1.left-100, band1.right+100), col="green")

7.1.2.1 genes on the first “band” of chromosome 12

# gene names
unique(chr12_genenames[between(start.A,band1.left, band1.right)]$gene.name)
## [1] "Methionine AminoPeptidase"       "CytiDine Deaminase"             
## [3] "Effect on Ras Function"          "Increased Recombination Centers"
# GO terms present
unique(chr12_genenames[between(start.A,band1.left-100, band1.right+100)]$GO.term)
##  [1] "proteolysis"                                                        
##  [2] "negative regulation of gene expression"                             
##  [3] "protein initiator methionine removal involved in protein maturation"
##  [4] "protein initiator methionine removal"                               
##  [5] "cytidine catabolic process"                                         
##  [6] "deoxycytidine catabolic process"                                    
##  [7] "pyrimidine-containing compound salvage"                             
##  [8] "cytidine deamination"                                               
##  [9] "protein targeting to membrane"                                      
## [10] "peptidyl-L-cysteine S-palmitoylation"                               
## [11] "protein palmitoylation"                                             
## [12] "double-strand break repair via nonhomologous end joining"           
## [13] "protein ubiquitination"                                             
## [14] "double-strand break repair via synthesis-dependent strand annealing"

7.1.3 Look at the second group

band2.left <- 8360186
plot_sorted_coordinates(
  coordinates_plot_cor[chr.A == 12],
  separator = separator,
  col = coordinates_plot_cor[chr.A==12]$col,
  xlim = c(band2.left, top_limit)
)
abline(v=c(band2.left, top_limit+1000), col="green")

There are 26 genes between the green lines

7.1.3.1 genes on the second band of chromsome 12

# gene names
unique(chr12_genenames[between(start.A,band2.left-1500, top_limit+1500)]$gene.name)
##  [1] "Long-Chain Base"                                                                            
##  [2] "Vacuolar Protein Sorting"                                                                   
##  [3] "Yeast Protein Two"                                                                          
##  [4] "REDuctional division"                                                                       
##  [5] "Ribosomal Protein of the Small subunit"                                                     
##  [6] "Nonhomologous End-Joining defective"                                                        
##  [7] "SECretory"                                                                                  
##  [8] "DeCapping Scavenger"                                                                        
##  [9] "Protein Interacting with Gsy2p"                                                             
## [10] NA                                                                                           
## [11] "mitochondrial protein Related to Spastic paraplegia with Optic atrophy and neuropathy SPG55"
## [12] "ChiTinaSe"                                                                                  
## [13] "Mitosis Entry Checkpoint"                                                                   
## [14] "General Control Derepressed"                                                                
## [15] "ExtraCellular Mutant"                                                                       
## [16] "EXo-1,3-beta-Glucanase"                                                                     
## [17] "UBiquitin-Conjugating"                                                                      
## [18] "AuTophaGy related"                                                                          
## [19] "SPa2 Homolog"                                                                               
## [20] "tRNA-specific Adenosine Deaminase"                                                          
## [21] "PEroXisome related"                                                                         
## [22] "Nicotinamide Mononucleotide Adenylyltransferase"                                            
## [23] "CHitin Synthase-related"
# GO terms present
unique(chr12_genenames[between(start.A,band2.left-1500, top_limit+1500)]$GO.term)
##   [1] "lipid metabolic process"                                                                         
##   [2] "sphingolipid metabolic process"                                                                  
##   [3] "response to heat"                                                                                
##   [4] "phosphorylation"                                                                                 
##   [5] "calcium-mediated signaling"                                                                      
##   [6] "lipid phosphorylation"                                                                           
##   [7] "protein targeting to vacuole"                                                                    
##   [8] "retrograde transport, vesicle recycling within Golgi"                                            
##   [9] "intracellular protein transport"                                                                 
##  [10] "retrograde vesicle-mediated transport, Golgi to endoplasmic reticulum"                           
##  [11] "intra-Golgi vesicle-mediated transport"                                                          
##  [12] "Golgi to endosome transport"                                                                     
##  [13] "protein transport"                                                                               
##  [14] "macroautophagy"                                                                                  
##  [15] "cytoplasm to vacuole transport by the Cvt pathway"                                               
##  [16] "Rab protein signal transduction"                                                                 
##  [17] "protein localization to phagophore assembly site"                                                
##  [18] "cellular protein-containing complex localization"                                                
##  [19] "retrograde transport, endosome to Golgi"                                                         
##  [20] "synaptonemal complex assembly"                                                                   
##  [21] "reciprocal meiotic recombination"                                                                
##  [22] "sporulation resulting in formation of a cellular spore"                                          
##  [23] "positive regulation of catalytic activity"                                                       
##  [24] "meiotic cell cycle"                                                                              
##  [25] "meiotic recombination checkpoint"                                                                
##  [26] "ribosomal small subunit assembly"                                                                
##  [27] "cytoplasmic translation"                                                                         
##  [28] "rRNA export from nucleus"                                                                        
##  [29] "translation"                                                                                     
##  [30] "maturation of SSU-rRNA"                                                                          
##  [31] "positive regulation of nuclear-transcribed mRNA catabolic process, deadenylation-dependent decay"
##  [32] "DNA repair"                                                                                      
##  [33] "double-strand break repair"                                                                      
##  [34] "double-strand break repair via nonhomologous end joining"                                        
##  [35] "cellular response to DNA damage stimulus"                                                        
##  [36] "homologous recombination"                                                                        
##  [37] "double-strand break repair via single-strand annealing"                                          
##  [38] "endoplasmic reticulum to Golgi vesicle-mediated transport"                                       
##  [39] "vesicle fusion"                                                                                  
##  [40] "vesicle-mediated transport"                                                                      
##  [41] "vesicle fusion with endoplasmic reticulum"                                                       
##  [42] "vesicle fusion with Golgi apparatus"                                                             
##  [43] "deadenylation-dependent decapping of nuclear-transcribed mRNA"                                   
##  [44] "cellular response to starvation"                                                                 
##  [45] "nuclear-transcribed mRNA catabolic process, deadenylation-independent decay"                     
##  [46] "positive regulation of exoribonuclease activity"                                                 
##  [47] "glycogen biosynthetic process"                                                                   
##  [48] "regulation of glycogen biosynthetic process"                                                     
##  [49] "regulation of phosphoprotein phosphatase activity"                                               
##  [50] "spliceosomal snRNP assembly"                                                                     
##  [51] "mRNA processing"                                                                                 
##  [52] "biological_process"                                                                              
##  [53] "RNA splicing"                                                                                    
##  [54] "translational termination"                                                                       
##  [55] "polysaccharide catabolic process"                                                                
##  [56] "septum digestion after cytokinesis"                                                              
##  [57] "carbohydrate metabolic process"                                                                  
##  [58] "chitin catabolic process"                                                                        
##  [59] "metabolic process"                                                                               
##  [60] "cell wall organization"                                                                          
##  [61] "regulation of cell cycle"                                                                        
##  [62] "DNA damage checkpoint"                                                                           
##  [63] "telomere maintenance via recombination"                                                          
##  [64] "telomere maintenance"                                                                            
##  [65] "double-strand break repair via homologous recombination"                                         
##  [66] "nucleotide-excision repair"                                                                      
##  [67] "chromatin silencing at telomere"                                                                 
##  [68] "cell cycle"                                                                                      
##  [69] "intra-S DNA damage checkpoint"                                                                   
##  [70] "mitotic DNA replication checkpoint"                                                              
##  [71] "meiotic DNA integrity checkpoint"                                                                
##  [72] "translational initiation"                                                                        
##  [73] "regulation of translation"                                                                       
##  [74] "regulation of translational initiation"                                                          
##  [75] "cellular metabolic process"                                                                      
##  [76] "regulation of catalytic activity"                                                                
##  [77] "proteolysis"                                                                                     
##  [78] "glutathione metabolic process"                                                                   
##  [79] "glutathione catabolic process"                                                                   
##  [80] "xenobiotic metabolic process"                                                                    
##  [81] "cellular glucan metabolic process"                                                               
##  [82] "glucan catabolic process"                                                                        
##  [83] "fungal-type cell wall organization"                                                              
##  [84] "protein ubiquitination"                                                                          
##  [85] "protein neddylation"                                                                             
##  [86] "autophagy"                                                                                       
##  [87] "autophagy of nucleus"                                                                            
##  [88] "reticulophagy"                                                                                   
##  [89] "conjugation"                                                                                     
##  [90] "bipolar cellular bud site selection"                                                             
##  [91] "pseudohyphal growth"                                                                             
##  [92] "regulation of cell shape"                                                                        
##  [93] "mating projection formation"                                                                     
##  [94] "invasive filamentous growth"                                                                     
##  [95] "positive regulation of MAPK cascade"                                                             
##  [96] "tRNA wobble adenosine to inosine editing"                                                        
##  [97] "tRNA modification"                                                                               
##  [98] "tRNA processing"                                                                                 
##  [99] "peroxisome organization"                                                                         
## [100] "ER-dependent peroxisome organization"                                                            
## [101] "membrane tubulation"                                                                             
## [102] "regulation of peroxisome organization"                                                           
## [103] "biosynthetic process"                                                                            
## [104] "NAD biosynthetic process"                                                                        
## [105] "pyridine nucleotide biosynthetic process"                                                        
## [106] "cellular bud site selection"                                                                     
## [107] "conjugation with cellular fusion"                                                                
## [108] "cell wall chitin catabolic process"                                                              
## [109] "regulation of transcription, DNA-templated"                                                      
## [110] "Golgi to plasma membrane transport"                                                              
## [111] "ascospore wall assembly"

8 Look at hotspots

hotspot_data <- fread("data/SI_Data_08_hotspotTableForPaper_topGenes100_170420_page1.csv")
hotspot_data.interval <- hotspot_data[,.(hotspotMarker, chromosome, bootstrapIntervalLeft, bootstrapIntervalRight)]
# remove "chr" part of the chromosome name
hotspot_data.interval$chromosome <-gsub('chr', '', hotspot_data.interval$chromosome)

# convert roman chromosome numbers to numbers
hotspot_data.interval$chromosome <- as.numeric(as.roman(hotspot_data.interval$chromosome))

9 Get hotspot locations ordered

vchr <- unique(hotspot_data.interval$chromosome)
hotspot_data.interval$left.map <- 0
hotspot_data.interval$right.map <- 0
for (i in 1:length(vchr)){
  if (vchr[i] != 1) {
    previous <- vchr[i-1]
    hotspot_data.interval[chromosome == vchr[i]]$left.map <- max(coordinates_plot_cor[chr.A==previous]$start.A) + separator + hotspot_data.interval[chromosome==vchr[i]]$bootstrapIntervalLeft
    hotspot_data.interval[chromosome == vchr[i]]$right.map <- max(coordinates_plot_cor[chr.A==previous]$start.A) + separator + hotspot_data.interval[chromosome==vchr[i]]$bootstrapIntervalRight
  } else if (vchr[i] == 1){
    hotspot_data.interval[chromosome == vchr[i]]$left.map <- hotspot_data.interval[chromosome==vchr[i]]$bootstrapIntervalLeft
    hotspot_data.interval[chromosome == vchr[i]]$right.map <- hotspot_data.interval[chromosome==vchr[i]]$bootstrapIntervalRight
  }
}

9.1 Get genes that are inside the hotspots

granges.A <- GRanges(seqnames = gene.location.order$chr.A,
        ranges=IRanges(start=gene.location.order$start.A,
                       end = gene.location.order$end.A),
        strand= gene.location.order$strand.A)
names(granges.A) <- gene.location.order$geneA
granges.A <- unique(granges.A)

granges.B <- GRanges(seqnames = gene.location.order$chr.B,
                     ranges=IRanges(start=gene.location.order$start.B,
                                    end = gene.location.order$end.B),
                     strand= gene.location.order$strand.B)
names(granges.B) <- gene.location.order$geneB
granges.B <- unique(granges.B)


granges.hotspots <- GRanges(seqnames = hotspot_data.interval$chromosome,
                            ranges=IRanges(start=hotspot_data.interval$bootstrapIntervalLeft,
                                           end=hotspot_data.interval$bootstrapIntervalRight))
names(granges.hotspots) <- hotspot_data.interval$hotspotMarker


# genes that are in hotspots
overlapping_genesA <- subsetByOverlaps(granges.A, granges.hotspots)
genes_in_hotspot <- unique(gene.location.order[geneA %in% names(overlapping_genesA)][,.(geneA, start.A, end.A, chr.A, strand.A)])

overlap <- findOverlaps(granges.A, granges.hotspots, ignore.strand=T)
genes_in_hotspot$hotspot <- names(granges.hotspots[subjectHits(overlap)])
genes_in_hotspot_poshot <-  merge(genes_in_hotspot, hotspot_data.interval[,.(hotspotMarker, bootstrapIntervalLeft, bootstrapIntervalRight, left.map, right.map)], by.x="hotspot", by.y="hotspotMarker")
genes_in_hotspot_pos <- unique(merge(genes_in_hotspot_poshot, coordinates_plot_cor[,.(geneA, start.A.map=start.A, end.A.map =end.A)],  by="geneA"))

Only 15.11% of the causal genes are in the hotspots.
Most of the causal genes were not located in the hotspots

9.1.0.1 number of genes in hotspots and number of hotspots in each chromosome in and how many causal genes are in each hotspot

# how many hotspots are in each chromosome and how many genes overlapping with hotspots are in each chromosome
hotspot_genes_in_chr <- genes_in_hotspot_pos[,.N, by=chr.A]
hotspots_in_chr <- hotspot_data.interval[,.N, by=chromosome]

hotspots_per_chr <- merge(hotspot_genes_in_chr, hotspots_in_chr, by.x="chr.A", by.y="chromosome")
setnames(hotspots_per_chr, c("chr", "n_genes", "n_hotspots"))
head(hotspots_per_chr[order(-n_genes)])
chr n_genes n_hotspots
4 13 12
7 12 9
15 11 10
12 9 6
13 9 8
10 8 8 
# how many causal genes are in the each hotspot (that contains causal genes) (ordered by number of genes)
head(genes_in_hotspot[,.(n_genes=.N),  by=hotspot][order(-n_genes)])
hotspot n_genes
chrIV:461656_A/G 8
chrXII:848842_A/G 6
chrII:619028_C/A 5
chrVII:178509_T/C 5
chrIX:105090_T/C 5
chrVII:850923_T/C 4

9.1.1 Plot number of hotspots that have a certain number of causal genes

ngenes_hotspot <- genes_in_hotspot[,.(n_genes=.N),  by=hotspot]
nhotspots_genes <- ngenes_hotspot[,.N, by=n_genes][order(n_genes)]
bp <- barplot(nhotspots_genes$N, names.arg = nhotspots_genes$n_genes, xlab = "Number of genes in hotspot", ylab="Number of hotspots", ylim=c(0,max(nhotspots_genes$N)+2))
text(bp, nhotspots_genes$N, labels=nhotspots_genes$N, pos = 3)

9.2 comparison of found causal genes in hotspots with genes from paper in hotspots

genes_hotspot_paper <- hotspot_data[,.(hotspotMarker, allGenesInInterval)]
genes_in_hotspot.names <- merge(genes_in_hotspot, unique(genes_GO.bio[,.(gene, symbol, gene.name)]), by.x="geneA", by.y="gene")

# genes_in_hotspot.names[geneA %in% unlist(strsplit(genes_hotspot_paper$allGenesInInterval, split = ";")) | symbol %in% unlist(strsplit(genes_hotspot_paper$allGenesInInterval, split = ";"))]

if (all(genes_in_hotspot.names$geneA %in% unlist(strsplit(genes_hotspot_paper$allGenesInInterval, split = ";")) | genes_in_hotspot.names$symbol %in% unlist(strsplit(genes_hotspot_paper$allGenesInInterval, split = ";")))){
  print("All the causal genes found in the hotspots have been reported by the paper")
} else {
  print("Some of the causal genes are not reported in the paper")
}
## [1] "All the causal genes found in the hotspots have been reported by the paper"

9.3 Genes that are being affected by genes in a hotspot

genes_affected_byhotspot <- unique(find.effects_TF[geneA %in% genes_in_hotspot$geneA][,.(geneA, geneB)])

In total, there are 1593 genes affected by genes in a hotspot

genes_affected_byhotspotB <- unique(merge(unique(gene.location.order[geneA %in% names(overlapping_genesA)]), genes_in_hotspot[,.(geneA, hotspot)], by="geneA")[,.(geneB, start.B, end.B, chr.B, hotspot, chr.A)])
toplot <- unique(genes_affected_byhotspotB[,.(chr.A,.N), hotspot][order(chr.A)])
color = grDevices::colors()[grep('gr(a|e)y', grDevices::colors(), invert = T)]
pal <- sample(color, length(unique(toplot$chr.A)))
toplot$col <-pal[toplot$chr.A]
barplot(
  toplot$N,
  names.arg = toplot$hotspot,
  las = 2,
  cex.names = 0.5,
  col=toplot$col, 
  main ="Number of genes each hotspot affects"
)
legend(x = "topright", legend = unique(toplot$chr.A), col = pal, pch=20, title="chr", cex = 0.7)

9.4 Plot gene 12 with described hotspots

plot_sorted_coordinates(
  coordinates_plot_cor[chr.A==12],
  separator = separator,
  col = coordinates_plot_cor[chr.A==12]$col
)


nCol <- nrow(hotspot_data.interval[chromosome==12])
col<- rep(1:nCol, each = 2)

hotspots <- data.table(c(hotspot_data.interval[chromosome==12]$left.map, hotspot_data.interval[chromosome==12]$right.map))
hotspots <- hotspots[order(V1)]
abline(v=unlist(hotspots$V1), 
       col=as.factor(col), lwd=1)

9.4.1 closer look

# genes that are affecting chromosome 3 + positions
# genes that are affecting chromosome 3 + positions
band1.left <- min(genesA_start_order$start.A)
band1.right <- 8525907
plot_sorted_coordinates(
  coordinates_plot_cor[chr.A == 12],
  separator = separator,
  col = coordinates_plot_cor[chr.A==12]$col,
  xlim = c(min(genesA_start_order$start.A), top_limit)
)
# abline(v=c(band1.left-1500, band1.right+1500), col="green")
abline(v=unlist(hotspots$V1), 
       col=as.factor(col), lwd=1.5)

9.4.2 genes in chromosome 12 that overlap with hotspots

unique(chr12_genenames[chr12_genenames[, geneA %in% genes_in_hotspot_pos[chr.A==12]$geneA]][,.(geneA, start.A, symbol, gene.name)])
geneA start.A symbol gene.name
YLL007C 7828644 LMO1 eLMO homolog
YLR273C 8383425 PIG1 Protein Interacting with Gsy2p
YLR275W 8388720 SMD2 NA
YLR361C-A 8543725 NA NA
YLR364W 8548404 GRX8 GlutaRedoXin
YLR369W 8553894 SSQ1 Stress-Seventy subfamily Q
YLR371W 8557056 ROM2 RhO1 Multicopy suppressor
YLR375W 8566039 STP3 protein with similarity to Stp1p
YLR376C 8567168 PSY3 Platinum SensitivitY

9.4.3 All hotspots plotted

plot_sorted_coordinates(
  coordinates_plot_cor,
  separator = separator,
  col = coordinates_plot_cor$col
)


nCol <- nrow(hotspot_data.interval)
col<- rep(1:nCol, each = 2)

hotspots <- data.table(c(hotspot_data.interval$left.map, hotspot_data.interval$right.map))
hotspots <- hotspots[order(V1)]
abline(v=unlist(hotspots$V1), 
       col=as.factor(col), lwd=0.5)

9.4.4 Only hotspots that contain causal genes plotted

plot_sorted_coordinates(
  coordinates_plot_cor,
  separator = separator,
  col = coordinates_plot_cor$col
)


hotspots_causalgenes <- unique(genes_in_hotspot_pos[,.(hotspot, left.map, right.map)])
nCol <- nrow(hotspots_causalgenes)
col<- rep(1:nCol, each = 2)

hotspots <- data.table(c(hotspots_causalgenes$left.map, hotspots_causalgenes$right.map))
hotspots <- hotspots[order(V1)]
abline(v=unlist(hotspots$V1), 
       col=as.factor(col), lwd=0.5)

9.4.5 Color the gene pairs where the causal gene overlaps an eQTL hotspot

coordinates_plot_cor$hotspotcol <- "grey"
coordinates_plot_cor[geneA %in% genes_in_hotspot_pos$geneA]$hotspotcol <- "red"

plot_sorted_coordinates(
  coordinates_plot_cor,
  separator = separator,
  col = coordinates_plot_cor$hotspotcol
)

9.4.6 Color the gene pairs where either the causal gene overlaps the hotspot, the marker overlaps the hotspot or both

Color the plot if
* the causal gene overlaps the hotspot * the causal gene’s eQTL overlaps the hotspot * the causal gene and its eQTL overlaps the hotspot

colorbymarker <- unique(merge(find.effects_TF, unique(eqtl_results[,.(gene, pmarker, CI.l, CI.r)]), by.x=c("geneA", "eqtl.A"), by.y=c("gene", "pmarker")))

colorbymarker <- separate(colorbymarker, col = CI.l, c("marker.chr", "marker.pos.l", "marker.other.l"), sep = "[:_]+", remove = F,
         convert = T, extra = "warn", fill = "warn")
colorbymarker <- separate(colorbymarker, col = CI.r, c("marker.chr.r", "marker.pos.r", "marker.other.r"), sep = "[:_]+", remove = F,
                          convert = T, extra = "warn", fill = "warn")

# remove extra columns
colstoremove <-  c("CI.l", "CI.r", "marker.chr.r", "marker.other.l", "marker.other.r")
colorbymarker[,(colstoremove):=NULL]

## transform chromosome ids into numbers
# remove "chr" part of the chromosome name
colorbymarker$marker.chr <-gsub('chr', '', colorbymarker$marker.chr)


colorbymarker$marker.chr <- as.numeric(as.roman(colorbymarker$marker.chr))

colorbymarker_toGR <- unique(colorbymarker[,.(geneA, eqtl.A, marker.chr, marker.pos.l, marker.pos.r)])

granges.marker <- GRanges(seqnames = colorbymarker_toGR$marker.chr,
                     ranges=IRanges(start=colorbymarker_toGR$marker.pos.l,
                                    end = colorbymarker_toGR$marker.pos.r))
names(granges.marker) <- colorbymarker_toGR$eqtl.A

granges.hotspots <- GRanges(seqnames = hotspot_data.interval$chromosome,
                            ranges=IRanges(start=hotspot_data.interval$bootstrapIntervalLeft,
                                           end=hotspot_data.interval$bootstrapIntervalRight))
names(granges.hotspots) <- hotspot_data.interval$hotspotMarker


# markers that are in hotspots
overlapping_markers <- subsetByOverlaps(granges.marker, granges.hotspots)
markers_in_hotspot <- colorbymarker_toGR[eqtl.A %in% names(overlapping_markers)]

coordinates_plot_cor$hotspotcol <- "grey"
# cases where the gene and the marker overlap the hotspot
coordinates_plot_cor[geneA %in% markers_in_hotspot$geneA & geneA %in% genes_in_hotspot_pos$geneA]$hotspotcol <- "chartreuse4"
# cases where the gene overlaps the hotspot but the marker doesn't
coordinates_plot_cor[!geneA %in% markers_in_hotspot$geneA & geneA %in% genes_in_hotspot_pos$geneA]$hotspotcol <- "red"
# cases where the marker overlaps the hotspot but the gene doesn't
coordinates_plot_cor[geneA %in% markers_in_hotspot$geneA & !geneA %in% genes_in_hotspot_pos$geneA]$hotspotcol <- "cyan"


plot_sorted_coordinates(
  coordinates_plot_cor,
  separator = separator,
  col = coordinates_plot_cor$hotspotcol
)
add_legend("top", legend=c("gene and eQTL", "gene", "eqtl"), pch=20, 
           col=c("chartreuse4", "red", "cyan"),,
           horiz=TRUE, cex=0.8, title="Overlapping hotspot")

if (!exists("numlinks_from_gene")){
  numlinks_from_gene.A <- find.effects_TF[, .(geneB, count.A=.N), by=geneA]
  numlinks_from_gene.B <- find.effects_TF[, .(count.B=.N), by=geneB]
  numlinks_from_gene <- merge(numlinks_from_gene.A, numlinks_from_gene.B, by="geneB", all=T)
  setcolorder(numlinks_from_gene, c("geneA", "geneB", "count.A", "count.B"))
}
hist(numlinks_from_gene$count.A, main = "Frequency of links going out of causal genes")

see which genes that have more than 200 links going out overlap with the hotspots # See overlap of genes that have > 200 links going out with the hotspots Add number of links going out to the coordinates table

coordinates_plot_links <- merge(coordinates_plot_cor, unique(numlinks_from_gene[,.(geneA, count.A)])[order(-count.A)], by="geneA")

9.6 Values

Only 15.11% of the causal genes are in the hotspots.
Most of the causal genes were not located in the hotspots

In total, there are 1593 genes affected by genes in a hotspot

10 Read counts

load("data/counts.RData")
# matrix where 1 if the gene is absent in that individual (if the field's value is zero), 0 if it's present
# For all genes
getzeros <- apply(counts$pheno, 1, function(x) {ifelse(x==0, 1, 0)})
# sum the number of individuals that don't have that gene
howmany <- colSums(getzeros)
# How many samples each gene has missing
count_missinggene <- data.table(gene=names(howmany), nsamples_missing =howmany)
count_missinggene <- count_missinggene[order(-nsamples_missing)]

# How many samples each *causal* gene has missing
causalgenes_counts <- merge(unique(find.effects_TF[,.(geneA, eqtl.A)]), count_missinggene, by.x="geneA", by.y="gene")
causalgenes_counts <- causalgenes_counts[order(-nsamples_missing)]

# add coordinates and chromosome
causalgenes_counts.coord <- unique(merge(causalgenes_counts, coordinates_plot_cor[,.(geneA, start.A, end.A, chr.A)], by="geneA"))

# keep the genes that are in chromosome 12, 14 or 15 - chromosomes that show the strongest bands
causalgenes_counts.coord.bands <- causalgenes_counts.coord[chr.A %in% c(12,14, 15)][order(-nsamples_missing)]

# transform counts$pheno into dt and transposing
counts_pheno.dt <- data.table(t(counts$pheno), keep.rownames = T)

10.1 Violin plot of read counts for all causal genes in chr 12, 14 and 15

genes_to_plot.causal <- causalgenes_counts.coord.bands$geneA
counts_genes_to_plot.causal <- counts_pheno.dt[,..genes_to_plot.causal]

res <- data.table(NULL)
for (gene in names(counts_genes_to_plot.causal)){
  res <- rbind(res, data.table(gene, unlist(counts_pheno.dt[,..gene]), "causal", causalgenes_counts.coord.bands[geneA==gene]$chr.A))
}
setnames(res, c("gene", "counts", "causal", "chr"))


p <- ggplot(res, aes(x=gene, y=counts, fill=as.factor(chr))) + 
  geom_violin() + 
  labs(title="Counts distribution for causal genes in chr 12, 14 and 15")
p + theme(axis.text.x = element_text(angle = 90, hjust = 1))

10.2 Violin plot of read counts for causal genes in chr 12, 14 and 15 that have one or more individuals missing

# causal genes that genes that are in chr 12, 14 or 15 and are missing in at least one individual
genes_to_plot.causal <- causalgenes_counts.coord.bands[nsamples_missing>0]$geneA

# get read counts for those genes
counts_genes_to_plot.causal <- counts_pheno.dt[,..genes_to_plot.causal]

res.sub <- data.table(NULL)
for (gene in names(counts_genes_to_plot.causal)){
  res.sub <- rbind(res.sub, data.table(gene, unlist(counts_pheno.dt[,..gene]), "causal", causalgenes_counts.coord.bands[geneA==gene]$chr.A))
}
setnames(res.sub, c("gene", "counts", "causal", "chr"))


p.sub <- ggplot(res.sub, aes(x=gene, y=counts, fill=as.factor(chr))) + 
  geom_violin() + 
  ylim(0,200) +
  labs(title="Counts distribution for causal genes in chr 12, 14 and 15 where the gene is missing \n in at least one sample")
p.sub + theme(axis.text.x = element_text(angle = 90, hjust = 1))

10.3 Algorithm to find hotspots

  1. Find genes that are affecting at least 10 other genes
  2. Hotspot where there are 10 or more genes in a row that are affecting 10 other genes
causalgenes.pos.count1 <- unique(coordinates_plot_links[order(chr.A,start.A)][,.(geneA, start.A, end.A, chr.A, count.A)])

allgenes.location <- genepos

## transform chromosome ids into numbers
# remove "chr" part of the chromosome name
allgenes.location$chr.id <- gsub('chr', '', allgenes.location$chr.id)
# convert roman chromosome numbers to numbers
allgenes.location$chr.id <- as.numeric(as.roman(allgenes.location$chr.id))
# geneA with counts, sorted position and original position
causalgenes.pos.count <- merge(causalgenes.pos.count1, allgenes.location, by.x=c("geneA", "chr.A"), by.y=c("gene", "chr.id"))
causalgenes.pos.count <- causalgenes.pos.count[order(chr.A, start.A, end.A)]

# For each chromosome get the sequence of values that follow a certain condition
# condition == count.A>lim
# True - causal genes affect "lim" or more genes
# False - causal genes affect less than "lim" genes

# create list of tables - one for each chr
# each table has the runs for the condition count.A>lim

rle.res.list <- rle_causalgenes(causalgenes.pos.count, lim = 10)


# find hotspots and plot them
# pdf(file = "results/figures/my_causal_hotspots.pdf", onefile = T)
plot_sorted_coordinates(coordinates_plot_cor, separator = separator, col = coordinates_plot_cor[chr.A==chr]$col)

hot <- find_hotspots(rle.list = rle.res.list, coordinates_plot = coordinates_plot_cor, causalgenes = causalgenes.pos.count, lim = 10, plt = T)

# dev.off()

# save the hotspot intervals in a grange object (also available in a table)
granges_myhotspots <- GRanges(seqnames = hot$chr,
                            ranges=IRanges(start=hot$start,
                                           end=hot$end))
granges_myhotspots.original <- GRanges(seqnames = hot$chr,
                                       ranges=IRanges(start=hot$start_original,
                                                      end=hot$end_original))

10.3.1 Get genes in hotstpot

Get genes in hotspot with causal effect and genes without causal effect

# get granges for all the genes
allgenes.location[is.na(chr.id)]$chr.id <- 17
granges_allgenes <- GRanges(seqnames = allgenes.location$chr.id, 
                            ranges = IRanges(start=allgenes.location$chr.start, 
                                             end=allgenes.location$chr.end))
names(granges_allgenes) <- allgenes.location$gene

# get granges that define hotspots
granges_myhotspots.original <- GRanges(seqnames = hot$chr,
                                       ranges=IRanges(start=hot$start_original,
                                                      end=hot$end_original))

# granges for genes that are in a hotspot
granges_genesinhotspot <- subsetByOverlaps(granges_allgenes, granges_myhotspots.original)
# name hotspots
names(granges_myhotspots.original) <- paste0("h_", seqnames(granges_myhotspots.original), "_", ranges(granges_myhotspots.original))

genesinhotspot <- allgenes.location[gene %in% names(granges_genesinhotspot)]
genesinhotspot_notcausal <- genesinhotspot[!gene %in% causalgenes.pos.count$geneA]
# granges for causal genes in a hotspot
causalgenes_in_hotspot <- causalgenes.pos.count[geneA %in% genesinhotspot$gene]

10.3.2 Compare all genes in hotspots to random genes not in hotspots (read counts)

genes_to_plot.all <- genesinhotspot$gene
counts_genes_to_plot.all <- counts_pheno.dt[,..genes_to_plot.all]

res <- data.table(NULL)
for (ngene in names(counts_genes_to_plot.all)){
  res <- rbind(res, data.table(ngene, unlist(counts_pheno.dt[,..ngene]), ifelse(ngene %in% causalgenes_in_hotspot$geneA, "causal in hotspot", "not causal in hotspot"), genesinhotspot[gene==ngene]$chr.id))
}
setnames(res, c("gene", "counts", "causal", "chr"))

# genes not in hotspots and not causal & not a causal gene
cond1  <- data.table(genes = names(counts_pheno.dt[,2:ncol(counts_pheno.dt)]))
# in the dataset genes & not in a hotspot 
cond <- unlist(cond1[genes %in% allgenes.location$gene][!genes %in% genesinhotspot$gene][!genes %in% causalgenes.pos.count$geneA])

counts_notinhotspots <- counts_pheno.dt[,..cond]

# get random 10 genes that are not in hotspots or causal
set.seed(1)
random_genes_nothotspots <- sample(names(counts_notinhotspots), 30)

res.rand <- res
for (ngene in random_genes_nothotspots){
  res.rand <- rbind(res.rand, data.table(gene=ngene, counts=unlist(counts_pheno.dt[,..ngene]), causal="random", chr=allgenes.location[gene==ngene]$chr.id))
}

res.rand <- res.rand[order(chr)]

for (ch in unique(res.rand[causal != "random"]$chr)){
  p <- ggplot(res.rand[chr==ch | causal=="random"], aes(x=gene, y=counts)) + 
    geom_violin() + 
    facet_grid(~causal, scales = "free_x") +
    theme(axis.text.x = element_text(angle = 90, hjust = 1))+
    labs(title=paste0("Counts distribution for all genes in hotspots - chr", ch))
  print(p)
}

10.3.2.1 Count distribution of 60 random genes (not causal and not in hotspots)

set.seed(1)
random_genes_nothotspots <- sample(names(counts_notinhotspots), 60)

res.rand <- res
for (ngene in random_genes_nothotspots){
  res.rand <- rbind(res.rand, data.table(gene=ngene, counts=unlist(counts_pheno.dt[,..ngene]), causal="random", chr=allgenes.location[gene==ngene]$chr.id))
}

res.rand <- res.rand[order(chr)]


p <- ggplot(res.rand[causal=="random"], aes(x=gene, y=counts)) + 
    geom_violin() + 
    facet_grid(~causal, scales = "free_x") +
    theme(axis.text.x = element_text(angle = 90, hjust = 1))+
    labs(title="Counts distribution for 60 random genes")
print(p)

10.3.2.2 Count distribution causal genes in hotspots

for (ch in unique(res.rand[causal == "causal in hotspot"]$chr)){
  p <- ggplot(res.rand[(chr==ch & causal=="causal in hotspot") |   causal =="random"], aes(x=gene, y=counts)) + 
    geom_violin() + 
    facet_grid(~causal, scales = "free_x") +
    theme(axis.text.x = element_text(angle = 90, hjust = 1))+
    labs(title=paste0("Counts distribution for causal genes in hotspots - chr", ch))
  print(p)
}

10.3.2.3 Count distribution causal genes in hotspots vs causal genes not in hotspots (by chr)

causalgenes_notin_hotspot <- causalgenes.pos.count[!geneA %in% genesinhotspot$gene]

res.causal <- res
for (ngene in unique(causalgenes_notin_hotspot$geneA)){
  res.causal <- rbind(res.causal, data.table(gene=ngene, counts=unlist(counts_pheno.dt[,..ngene]), causal="causal not in hotspot", chr=allgenes.location[gene==ngene]$chr.id))
}
res.causal <- res.causal[order(chr)]

for (ch in unique(res.causal[causal=="causal in hotspot"]$chr)){
  p <- ggplot(res.causal[chr==ch & causal %in% c("causal in hotspot", "causal not in hotspot")], aes(x=gene, y=counts)) + 
    geom_violin() + 
    facet_grid(~causal, scales = "free_x") +
    theme(axis.text.x = element_text(angle = 90, hjust = 1))+
    labs(title=paste0("Counts distribution for causal genes in hotspots - chr", ch))
  print(p)
}

10.3.2.4 Count distribution causal genes in hotspots vs causal genes not in hotspots

p <- ggplot(res.causal[causal %in% c("causal in hotspot", "causal not in hotspot")], aes(x=gene, y=counts)) + 
  geom_violin() + 
  facet_grid(~causal, scales = "free_x") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))+
  labs(title="Counts distribution for causal genes in hotspots vs not in hotspots")
print(p)

10.3.3 Get hotspot name for each causal gene in hotspot

# get grange for causal genes in hotspots
causalgenes_in_hotspot.grange <- granges_genesinhotspot[names(granges_genesinhotspot) %in% causalgenes_in_hotspot$geneA] 

hot_overlaps <- findOverlaps(causalgenes_in_hotspot.grange, granges_myhotspots.original)
hot_overlaps.dt <- data.table(query=queryHits(hot_overlaps), subject=subjectHits(hot_overlaps))
hot_overlaps.dt$gnames <- names(causalgenes_in_hotspot.grange)[queryHits(hot_overlaps)]

# Add hotspot name to causal genes in hotspot
causalgenes_in_hotspot.hotspot <- merge(causalgenes_in_hotspot, hot_overlaps.dt, by.x="geneA", by.y="gnames")
causalgenes_in_hotspot.hotspot$hotspot <- names(granges_myhotspots.original)[causalgenes_in_hotspot.hotspot$subject]

# causalgenes_in_hotspot.hotspot[geneA %in% hot_overlaps.dt$gnames]$hotspot <- names(granges_myhotspots.original[hot_overlaps.dt$subject])
causalgenes_in_hotspot.hotspot[,c("query", "subject"):=NULL]

10.3.4 Plot number of unique genes affected by each hotspot

# can have repeated affected genes (if two causal genes affect the same gene)
# n_affectedby_hotspot <- causalgenes_in_hotspot.hotspot[, sum(count.A), by=hotspot]

# number of unique genes being affected
n_affectedby_hotspot.geneB <- merge(causalgenes_in_hotspot.hotspot, unique(find.effects_TF[,.(geneA, geneB)]), all.x=T, by="geneA")
n_affectedby_hotspot <- unique(n_affectedby_hotspot.geneB[,.(geneB, hotspot)])[,.N, by=hotspot]


g.ramp <- gray.colors(length(n_affectedby_hotspot$hotspot), start = 0, end = 1)
bp <- barplot(n_affectedby_hotspot$N, 
        col = g.ramp, 
        names.arg = n_affectedby_hotspot$hotspot, 
        las=2, 
        cex.names = 0.5, 
        main="Num of links out of each hotspot", 
        ylim=c(0,max(n_affectedby_hotspot$N)+1500))
# add numbers on top of the bars
text(
  bp,
  n_affectedby_hotspot$N,
  n_affectedby_hotspot$N,
  pos = 3
)

The number of links might be higher than the number of genes in the dataset since several genes in the same hotspot might be affecting the same gene, which the plot does not take into account

10.3.4.1 Get number of different genes and eqtls in each hotspot

causalgenes_in_hotspot.hot.eqtl <- merge(unique(find.effects_TF[,.(geneA, eqtl.A)]), causalgenes_in_hotspot.hotspot, by="geneA")

num_eqtls_hotspot <-unique(causalgenes_in_hotspot.hot.eqtl[,.(eqtl.A, hotspot)])[,.N, by=hotspot]
num_genes_hotspot <-causalgenes_in_hotspot.hot.eqtl[,.(geneA, hotspot)][,.N, by=hotspot]

num_genes_eqtls_hotspot <- merge(num_eqtls_hotspot, num_genes_hotspot, by="hotspot")
colnames(num_genes_eqtls_hotspot) <- c("hotspot", "num.eqtls", "num.genes") 
num_genes_eqtls_hotspot
hotspot num.eqtls num.genes
h_12_570776-830364 32 37
h_13_264541-390732 18 19
h_14_220659-669591 28 30
h_15_70325-240094 15 15
h_7_53787-191806 18 18
h_8_51111-211978 27 30

10.4 Number of individuals in each gene with expression <=10

# for each gene, get number of samples that has expression <= 10
getless10 <- apply(counts$pheno, 1, function(x) {ifelse(x<=10, 1, 0)})
howmany_less10 <- colSums(getless10)
count_less10 <- data.table(gene=names(howmany_less10), nsamples_less10 =howmany_less10)
count_less10 <- count_less10[order(-nsamples_less10)]

# get fraction of samples have expr <=10 for each gene
count_less10[,fraction:= nsamples_less10/ncol(counts$pheno)]

# subset to get the causal genes
fract_less10 <- count_less10[gene %in% causalgenes.pos.count$geneA]

# add causal category - causal in hotspot or causal not in hotspot
fract_less10[,causal:=ifelse(gene %in% causalgenes_in_hotspot.hotspot$geneA, "causal in hotspot", "causal not in hotspot")]

# subset to get the genes with no causal effects
count_less10_notcausal <- count_less10[!gene %in% find.effects_TF$geneA & gene %in% allgenes.location$gene]

# add causal category - not causal in hotspot or not causal not in hotspot
count_less10_notcausal[,causal := ifelse(gene %in% names(counts_notinhotspots), "not causal not in hotspot", "not causal in hotspot")]

# join the two groups - genes with causal effects and without
fract_less10_toplot <- rbind(fract_less10, count_less10_notcausal)

# boxplot of fraction of samples with expr <=10, separated by causal category 
box <- ggplot(fract_less10_toplot, aes(x=causal, y=fraction)) + 
  geom_boxplot()+
  # scale_y_continuous(trans='log10')
  # scale_y_log10()+
  ggtitle("Fraction individuals with expression <=10 per gene")
plot(box)

# boxplot of fraction of samples with expr <=10, separated by causal category + log10 scale
breaks <- seq(0,1,0.25)
box <- ggplot(fract_less10_toplot, aes(x=causal, y=fraction)) + 
  geom_boxplot()+
  scale_y_log10("fraction (log10) - showing original values", breaks = breaks, labels = breaks)+
  # scale_y_log10("fraction (log10)", oob=squish_infinite, breaks = breaks, labels = breaks)+ # use squish_infinite to "squish infitite values into range"
  ggtitle("Fraction individuals with expression <=10 per gene")

plot(box)
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 4565 rows containing non-finite values (stat_boxplot).

# violing plot
violin <- ggplot(fract_less10_toplot, aes(x=causal, y=fraction)) + 
  geom_violin() +
  ggtitle("Fraction individuals with expression <=10 per gene")

plot(violin)

The genes that are not in a hotspot seem to have less individuals with expression <=10 (?)

10.5 Histogram of fraction individuals with expression <=10 per gene

hist <- ggplot(fract_less10_toplot[causal %in% c("causal in hotspot", "not causal not in hotspot")], aes(x=fraction, color=causal, fill=causal)) + 
  geom_histogram(alpha=0.3, position="identity")+
  ggtitle("Fraction individuals with expression <=10 per gene") + 
  scale_y_sqrt()+
  # theme(legend.position="top")+ 
  coord_flip()
plot(hist)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

10.5.1 Get sample of not causal not in hotspot that is the same size as causal in hotspot

# take random sample of "not causal not in hotspot" the same size as causal in hotspot
nsample <- length(fract_less10_toplot[causal=="causal in hotspot"]$gene)
notcausal_nothotspot_sample <- sample(fract_less10_toplot[causal=="not causal not in hotspot"]$gene, nsample)

# plot histogram
hist <- ggplot(fract_less10_toplot[causal %in% c("causal in hotspot") | (causal %in% "not causal not in hotspot" & gene %in% notcausal_nothotspot_sample)], aes(x=fraction, color=causal, fill=causal)) + 
  geom_histogram(alpha=0.3, position="identity")+
  ggtitle("Fraction individuals with expression <=10 per gene") + 
  scale_y_sqrt()+
  # theme(legend.position="top")+ 
  coord_flip()
plot(hist)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

10.6 Chi-square test

H0: the categories are independent. H1: the categories are not independent

table.chi2 <- table(fract_less10_toplot$fraction, fract_less10_toplot$causal)
chi2.result <- chisq.test(table.chi2)
## Warning in chisq.test(table.chi2): Chi-squared approximation may be incorrect
print(chi2.result)
## 
##  Pearson's Chi-squared test
## 
## data:  table.chi2
## X-squared = 1600.1, df = 1437, p-value = 0.001608
if (chi2.result$p.value < 0.01){
  print(paste0("since the p-value (",round(chi2.result$p.value, digits = 4), ") is less than the significance level 0.01, we reject the null hypothesis - there's an association between the causal categories")) 
} else {
  print("We don't reject the null hypothesis: there is no association between the causal categories")
}
## [1] "since the p-value (0.0016) is less than the significance level 0.01, we reject the null hypothesis - there's an association between the causal categories"

Testing the difference between “causal in hotspot” and “not causal not in hotspot”

table.chi2 <- table(fract_less10_toplot[causal %in% c("causal in hotspot", "not causal not in hotspot")]$fraction, fract_less10_toplot[causal %in% c("causal in hotspot", "not causal not in hotspot")]$causal)
chi2.result <- chisq.test(table.chi2)
## Warning in chisq.test(table.chi2): Chi-squared approximation may be incorrect
print(chi2.result)
## 
##  Pearson's Chi-squared test
## 
## data:  table.chi2
## X-squared = 563.74, df = 417, p-value = 2.05e-06
if (chi2.result$p.value < 0.01){
  print(paste0("since the p-value (",round(chi2.result$p.value, digits = 4), ") is less than the significance level 0.01, we reject the null hypothesis - there's an association between the causal categories")) 
} else {
  print("We don't reject the null hypothesis: there is no association between the causal categories")
}
## [1] "since the p-value (0) is less than the significance level 0.01, we reject the null hypothesis - there's an association between the causal categories"

10.7 GO enrichment for causal genes in hotspots

which universe - causal genes + affected genes or only causal genes?

if (!exists("genes_GO.bio")){
  genes_GO.table <- fread("results/genelistwithGOterm.txt")
  genes_GO.table <- unique(genes_GO.table)
  genes_GO.bio   <- unique(genes_GO.table[GO.namespace=="biological_process"])
}

# create geneset
goframeData <- unique(genes_GO.bio[,.(GO.identifier, evidence, gene)])
gs <- getgeneset(goframeData)


causal.hotspot <- unlist(unique(causalgenes_in_hotspot.hot.eqtl$geneA))

# universe is genes involved in causality
universe <- unique(find.effects_TF$geneA)
universe <- unique(c(find.effects_TF$geneA, find.effects_TF$geneB))

# get enrichment for genesA
res.causalhotspot <- getenrichment(gs, universe = universe, interestinggenes = causal.hotspot, cond = T)
res.causalhotspot.dt <- data.table(summary(res.causalhotspot))
res.causalhotspot.dt
GOBPID Pvalue OddsRatio ExpCount Count Size Term
GO:0048869 0.0036080 2.444776 7.0427456 15 115 cellular developmental process
GO:0046688 0.0037269 Inf 0.1224825 2 2 response to copper ion
GO:0032482 0.0065644 11.712329 0.4286889 3 7 Rab protein signal transduction
GO:0035825 0.0090705 4.630311 1.3473079 5 22 homologous recombination
GO:0007049 0.0098659 1.783646 16.4126593 26 268 cell cycle
GO:0006032 0.0107298 31.061225 0.1837238 2 3 chitin catabolic process
GO:0046348 0.0107298 31.061225 0.1837238 2 3 amino sugar catabolic process
GO:0006281 0.0111363 2.251977 6.4915742 13 106 DNA repair
GO:0034293 0.0123384 3.167606 2.5721332 7 42 sexual sporulation
GO:0007165 0.0164184 1.961267 9.0637074 16 148 signal transduction
GO:1903046 0.0175392 2.506704 4.0449251 9 68 meiotic cell cycle process
GO:0045132 0.0185921 3.741733 1.5922729 5 26 meiotic chromosome segregation
GO:0000712 0.0205989 15.523810 0.2449651 2 4 resolution of meiotic recombination intermediates
GO:0045116 0.0205989 15.523810 0.2449651 2 4 protein neddylation
GO:0030435 0.0208933 2.424334 4.1644061 9 68 sporulation resulting in formation of a cellular spore
GO:0009653 0.0209187 2.422993 4.1645308 9 69 anatomical structure morphogenesis
GO:0000003 0.0210568 1.858722 10.1048089 17 165 reproduction
GO:0050789 0.0220923 1.459015 41.6440608 53 680 regulation of biological process
GO:0008360 0.0258120 5.845890 0.6736539 3 11 regulation of cell shape
GO:0044703 0.0292953 2.266326 4.4093711 9 72 multi-organism reproductive process
GO:0007264 0.0324516 2.776635 2.4496506 6 40 small GTPase mediated signal transduction
GO:0000272 0.0329018 5.194064 0.7348952 3 12 polysaccharide catabolic process
GO:0000917 0.0329018 5.194064 0.7348952 3 12 division septum assembly
GO:0046475 0.0329612 10.344671 0.3062063 2 5 glycerophospholipid catabolic process
GO:0006895 0.0329612 10.344671 0.3062063 2 5 Golgi to endosome transport
GO:0034765 0.0329612 10.344671 0.3062063 2 5 regulation of ion transmembrane transport
GO:0006560 0.0329612 10.344671 0.3062063 2 5 proline metabolic process
GO:0033313 0.0329612 10.344671 0.3062063 2 5 meiotic cell cycle checkpoint
GO:0061014 0.0329612 10.344671 0.3062063 2 5 positive regulation of mRNA catabolic process
GO:0035335 0.0329612 10.344671 0.3062063 2 5 peptidyl-tyrosine dephosphorylation
GO:0000076 0.0329612 10.344671 0.3062063 2 5 DNA replication checkpoint
GO:0006333 0.0357252 3.678702 1.2860666 4 21 chromatin assembly or disassembly
GO:0030010 0.0357252 3.678702 1.2860666 4 21 establishment of cell polarity
GO:0030437 0.0361592 2.696104 2.5108919 6 41 ascospore formation
GO:0051301 0.0381178 1.912993 6.8590218 12 112 cell division
GO:0050896 0.0387948 1.429679 32.7028360 42 534 response to stimulus
GO:0006310 0.0433974 2.201396 4.0016591 8 67 DNA recombination
GO:0051276 0.0467898 1.597974 13.5955610 20 222 chromosome organization
GO:0045003 0.0474786 7.755102 0.3674476 2 6 double-strand break repair via synthesis-dependent strand annealing
GO:0000741 0.0474786 7.755102 0.3674476 2 6 karyogamy
GO:0006474 0.0474786 7.755102 0.3674476 2 6 N-terminal protein amino acid acetylation
GO:0046834 0.0474786 7.755102 0.3674476 2 6 lipid phosphorylation
GO:0030473 0.0474786 7.755102 0.3674476 2 6 nuclear migration along microtubule
GO:0051647 0.0474786 7.755102 0.3674476 2 6 nucleus localization
GO:0007018 0.0474786 7.755102 0.3674476 2 6 microtubule-based movement
GO:0010970 0.0474786 7.755102 0.3674476 2 6 transport along microtubule
GO:0035435 0.0474786 7.755102 0.3674476 2 6 phosphate ion transmembrane transport
GO:0000492 0.0474786 7.755102 0.3674476 2 6 box C/D snoRNP assembly
GO:0007346 0.0492007 2.139680 4.1031648 8 67 regulation of mitotic cell cycle
GO:0000902 0.0497748 4.245953 0.8573777 3 14 cell morphogenesis
GO:0022603 0.0497748 4.245953 0.8573777 3 14 regulation of anatomical structure morphogenesis

11 Some comparisons with paper

11.1 which genes from the paper have causal effects

(not testing all for now)

# genes mentioned in the paper
ge <- data.table(gene =   c("YNL132W", "YNL199C", "YNL198C", "YNL197C","YNL196C", "YNL085W", "YOL081W", "YHR005C", "YHR178W", "YHR032W", "YFL021W", "YOR032C", "YKL015W", "YLR176C", "YCR018C", "YBR150C", "YOR172W", "YMR019W", "YLR256W", "YNL085W", "YHR005C", "YBR158W", "YDR160W"),
                 symbol = c("KRE33",   "GCR2",    "YNL198C", "WHI3",   "SLZ1",    "MKT1",    "IRA2",    "TIM10",   "STB5",    "ERC1" ,   "GAT1", "HMS1", "PUT3", "RFX1", "SRD1","TBS1", "YRM1","STB4", "HAP1", "MKT1", "GPA1", "AMN1", "SSY1"))



causalgenes.pos.count.name <- merge(causalgenes.pos.count, unique(genes_GO.bio[,.(gene, symbol, gene.name)]), by.x="geneA", by.y="gene", all.x=T)
causalgenes.pos.count.name[geneA %in% ge$gene]
geneA chr.A start.A end.A count.A chr.strand chr.start chr.end symbol gene.name
YBR150C 2 801806 808680 3 -1 541209 544493 TBS1 ThiaBendazole Sensitive
YBR158W 2 817146 822385 4 1 556549 558198 AMN1 Antagonist of Mitotic exit Network
YCR018C 3 1292508 1297662 16 -1 148238 148903 SRD1 NA
YHR005C 8 5298022 5307569 68 -1 113499 114917 GPA1 G Protein Alpha subunit
YHR032W 8 5357867 5367741 27 1 173344 175089 ERC1 Ethionine Resistance Conferring
YHR178W 8 5643822 5654182 7 1 459299 461530 STB5 Sin Three Binding protein
YMR019W 13 9162310 9180579 49 1 312156 315005 STB4 Sin Three Binding protein
YOR172W 15 11304241 11323306 10 1 654210 656570 YRM1 Yeast Reveromycin resistance Modulator

11.2 which genes are in a causal hotspot

causalgenes_in_hotspot.hot.eqtl[geneA %in% causalgenes.pos.count.name[geneA %in% ge$gene]$geneA]
geneA eqtl.A chr.A start.A end.A count.A chr.strand chr.start chr.end hotspot
YHR005C chrVIII:105298_T/A 8 5298022 5307569 68 -1 113499 114917 h_8_51111-211978
YHR032W chrVIII:172443_G/A 8 5357867 5367741 27 1 173344 175089 h_8_51111-211978
YMR019W chrXIII:313064_C/T 13 9162310 9180579 49 1 312156 315005 h_13_264541-390732 
find.effects_TF.names <- merge(unique(find.effects_TF[,.(geneA, geneB)]), unique(genes_GO.bio[,.(gene, symbol, gene.name, GO.identifier, GO.term)]), by.x="geneB", by.y="gene", allow.cartesian=T)
setnames(find.effects_TF.names, old=c("symbol", "gene.name", "GO.identifier", "GO.term"), new=c("symbol.B", "gene.name.B", "GO.identifier.B", "GO.term.B"))
find.effects_TF.names <- merge(find.effects_TF.names, unique(genes_GO.bio[,.(gene, symbol, gene.name, GO.identifier, GO.term)]), by.x="geneA", by.y="gene", allow.cartesian=T)
setcolorder(find.effects_TF.names, neworder = c("geneA", "geneB", "symbol", "symbol.B", "gene.name", "GO.identifier", "GO.term"))

# find.effects_TF.names[symbol=="ERC1"]

The paper mentions that “the ERC1 frameshift variant is linked to reduced mean expression levels of multiple genes in the methionine biosynthesis pathway” Is ERC1 affecting any gene involved in the methionine biosynthesis pathway?

find.effects_TF.names[symbol=="ERC1" & grepl("methionine",GO.term.B, fixed = F)]
geneA geneB symbol symbol.B gene.name GO.identifier GO.term gene.name.B GO.identifier.B GO.term.B
YHR032W YGR055W ERC1 MUP1 Ethionine Resistance Conferring GO:0006556 S-adenosylmethionine biosynthetic process Methionine UPtake GO:1903692 methionine import across plasma membrane
YHR032W YGR055W ERC1 MUP1 Ethionine Resistance Conferring GO:0042908 xenobiotic transport Methionine UPtake GO:1903692 methionine import across plasma membrane
YHR032W YGR055W ERC1 MUP1 Ethionine Resistance Conferring GO:0055085 transmembrane transport Methionine UPtake GO:1903692 methionine import across plasma membrane
YHR032W YGR204W ERC1 ADE3 Ethionine Resistance Conferring GO:0006556 S-adenosylmethionine biosynthetic process ADEnine requiring GO:0009086 methionine biosynthetic process
YHR032W YGR204W ERC1 ADE3 Ethionine Resistance Conferring GO:0042908 xenobiotic transport ADEnine requiring GO:0009086 methionine biosynthetic process
YHR032W YGR204W ERC1 ADE3 Ethionine Resistance Conferring GO:0055085 transmembrane transport ADEnine requiring GO:0009086 methionine biosynthetic process
YHR032W YIL046W ERC1 MET30 Ethionine Resistance Conferring GO:0006556 S-adenosylmethionine biosynthetic process METhionine requiring GO:0009086 methionine biosynthetic process
YHR032W YIL046W ERC1 MET30 Ethionine Resistance Conferring GO:0042908 xenobiotic transport METhionine requiring GO:0009086 methionine biosynthetic process
YHR032W YIL046W ERC1 MET30 Ethionine Resistance Conferring GO:0055085 transmembrane transport METhionine requiring GO:0009086 methionine biosynthetic process
YHR032W YIR017C ERC1 MET28 Ethionine Resistance Conferring GO:0006556 S-adenosylmethionine biosynthetic process METhionine GO:0009086 methionine biosynthetic process
YHR032W YIR017C ERC1 MET28 Ethionine Resistance Conferring GO:0042908 xenobiotic transport METhionine GO:0009086 methionine biosynthetic process
YHR032W YIR017C ERC1 MET28 Ethionine Resistance Conferring GO:0055085 transmembrane transport METhionine GO:0009086 methionine biosynthetic process
YHR032W YKR069W ERC1 MET1 Ethionine Resistance Conferring GO:0006556 S-adenosylmethionine biosynthetic process METhionine requiring GO:0009086 methionine biosynthetic process
YHR032W YKR069W ERC1 MET1 Ethionine Resistance Conferring GO:0042908 xenobiotic transport METhionine requiring GO:0009086 methionine biosynthetic process
YHR032W YKR069W ERC1 MET1 Ethionine Resistance Conferring GO:0055085 transmembrane transport METhionine requiring GO:0009086 methionine biosynthetic process

ERC1 is affecting 5 genes that have a GO term associated with the methionine pathway. The genes are YGR055W, YGR204W, YIL046W, YIR017C, YKR069W (MUP1, ADE3, MET30, MET28, MET1)

The paper mentions that the most known causal variants underlying yeast eQTL hotspots are coding. Genes -> HAP1, MKT1, GPA1, AMN1, SSY1

# with causal effect
causalgenes.pos.count.name[symbol %in% c("HAP1", "MKT1", "GPA1", "AMN1","SSY1")]
geneA chr.A start.A end.A count.A chr.strand chr.start chr.end symbol gene.name
YBR158W 2 817146 822385 4 1 556549 558198 AMN1 Antagonist of Mitotic exit Network
YHR005C 8 5298022 5307569 68 -1 113499 114917 GPA1 G Protein Alpha subunit 
# in hotspot
causalgenes_in_hotspot.hot.eqtl[geneA %in% causalgenes.pos.count.name[symbol %in% c("HAP1", "MKT1", "GPA1", "AMN1","SSY1")]$geneA]
geneA eqtl.A chr.A start.A end.A count.A chr.strand chr.start chr.end hotspot
YHR005C chrVIII:105298_T/A 8 5298022 5307569 68 -1 113499 114917 h_8_51111-211978

12 Heritability

Get causal genes with heritability >=0.9

According to the paper, “The single eQTLs that by themselves generated heritability of >=0.9 were all local eQTLs for genes in regions of the genome with high, structurally complex variation”.
By looking for causal genes that have heritability >= 0.9, I can find genes with structurally commplex variation

heritability_excel <- read_excel_allsheets("data/SI_Data_05_heritability.xlsx")
for_heritability <- heritability_excel$heritability_A
for_heritability[,h:=(A/(A+E))]

heritability <- merge(causalgenes.pos.count.name, for_heritability, by.x="geneA", by.y="...1")

heritability[h >= 0.9]
geneA chr.A start.A end.A count.A chr.strand chr.start chr.end symbol gene.name A E h
YCR039C 3 1343816 1348937 12 -1 199546 200178 MATALPHA2 MATing type protein ALPHA 1.3876988 0.1234378 0.9183146
YCR040W 3 1344712 1349728 16 1 200442 200969 MATALPHA1 MATing type protein ALPHA 1.4727897 0.0706636 0.9542172
YHL044W 8 5198088 5206924 5 1 13565 14272 NA NA 1.4451590 0.1414489 0.9108482
YLR179C 12 8208450 8223516 5 -1 514108 514713 NA NA 0.9825025 0.0766358 0.9276432 
# find if there are ENA genes in the causal genes
heritability[grepl("ENA", symbol)]
geneA chr.A start.A end.A count.A chr.strand chr.start chr.end symbol gene.name A E h
YDR040C 4 2039040 2047187 4 -1 535192 538467 ENA1 Exitus NAtru (Latin, “exit sodium”) 1.092867 0.1626103 0.8704793

12.1 Chi-square heritability

H0: The two categories have the same number of genes above and below hcut
H1: They do not

Testing if the expression of the causal genes is more/less heritable than the non-causal ones

chi2.causal <- data.table(for_heritability)
chi2.causal[,causal := ifelse(...1 %in% causalgenes.pos.count.name$geneA, "causal", "not causal")]

h.cutoff <- seq(0.5,0.95,0.05)
chi2.res <- data.table(h.cutoff, pval=numeric())
for (hcut in h.cutoff){
  higher <- chi2.causal[h>=hcut, .N, by="causal"][order(causal)]
  lower <- chi2.causal[h<hcut, .N, by="causal"][order(causal)]
  
  table.chi2 <- data.table(causal=c("causal", "not causal"),higher=higher$N, lower=lower$N)
  table.chi2 <- transpose(table.chi2, keep.names = "cond", make.names = "causal")
  
  chi2.result <- chisq.test(table.chi2[,2:ncol(table.chi2)])
  chi2.res[h.cutoff==hcut]$pval <- chi2.result$p.value
}
print(chi2.res)
##     h.cutoff         pval
##  1:     0.50 8.005096e-58
##  2:     0.55 1.038775e-49
##  3:     0.60 1.412253e-31
##  4:     0.65 1.718501e-28
##  5:     0.70 9.750816e-24
##  6:     0.75 4.354398e-15
##  7:     0.80 1.717123e-08
##  8:     0.85 3.761463e-03
##  9:     0.90 1.000000e+00
## 10:     0.95 1.000000e+00

13 Validate causal pairs by comparing with list of eqtls

If a gene-eqtl pair is in the reported list of genes with eqtls, then it means that the eqtl is affecting the gene. Applied to our causal genes: the eqtl of geneA is affecting gene B == geneA is affecting gene B. If the eqtl of geneA is in the reported list paired with geneB, then there’s a confirmation that geneA affects geneB

# add chr to find.effects_TF
find.effects_TF.chr <- unique(merge(find.effects_TF, coordinates_plot_cor[,.(geneA, geneB, chr.A, chr.B)], by=c("geneA", "geneB")), all.x=T)

# find cases in the reported gene-eqtl pairs where the eqtl of a causal gene is an eqtl for an affected gene
find.effects_TF.trans <- unique(merge(find.effects_TF.chr, eqtl_results[,.(gene, pmarker, cis)], by.x=c("geneB", "eqtl.A"), by.y=c("gene", "pmarker")), all.x=T)

Out of 28466 geneA affecting geneB pairs, 589 geneB-eqtlA pairs were in the reported list.
There are 2 cases where the gene and eqtl are in cis and 587 where eqtlA is in trans with geneB

14 Others

14.0.1 Get distance between gene and eqtl

if (!exists("genepos")){
  genepos <- fread("results/gene_pos.gz")
}
if (!exists("find.effects_TF")){
  find.effects_TF <- fread("results/findeffects_TF_newparams.gz")
}

# Add chomosome and start position to each gene
causal.pos.A <- merge(find.effects_TF, genepos, by.x="geneA", by.y="gene", all.x=T)
colnames(causal.pos.A) <- c("geneA", "geneB", "eqtl.A", "eqtl.B", "A->B", "B->A", "strand.A", "start.A","end.A", "chr.A")
causal.pos.B <- merge(causal.pos.A, genepos, by.x="geneB", by.y="gene", all.x=T)

# separate eqtl name into chromosome number and position of the eqtl
causal.pos.eqtlA <- separate(causal.pos.B, eqtl.A, c("eqtlA.chr", "eqtlA.pos", "eqtlA.other"), sep = "[:_]+", remove = F,
                             convert = T, extra = "warn", fill = "warn")
causal.pos.eqtlB <- separate(causal.pos.eqtlA, eqtl.B, c("eqtlB.chr", "eqtlB.pos", "eqtlB.other"), sep = "[:_]+", remove = F,
                             convert = T, extra = "warn", fill = "warn")

# tidy up
colstoremove <-  c("eqtlA.other", "eqtlB.other", "strand.A", "chr.strand")
causal.pos.eqtlB[,paste0(colstoremove):=NULL]
setnames(causal.pos.eqtlB, c("geneB", "geneA","eqtl.A","eqtlA.chr", "eqtlA.pos", 
                             "eqtl.B","eqtlB.chr", "eqtlB.pos", "A->B", "B->A", 
                             "start.A","end.A", "chr.A", "start.B","end.B","chr.B"))
setcolorder(causal.pos.eqtlB, c("geneA", "geneB", "eqtl.A", "eqtl.B","chr.A", 
                                "start.A","end.A","chr.B", "start.B","end.B","eqtlA.chr", 
                                "eqtlA.pos", "eqtlB.chr", "eqtlB.pos"))

# remove "chr" from chromosome number
cols = c("chr.A","chr.B", "eqtlA.chr", "eqtlB.chr")   # define which columns to work with
causal.pos.eqtlB[ , (cols) := lapply(.SD, function(x) {gsub("chr", "", x)}), .SDcols = cols] # replace
# "translate" into roman numerals into numeric
causal.pos.eqtlB[ , (cols) := lapply(.SD, function(x) {as.numeric(as.roman(x))}), .SDcols = cols]

# calculate distance between gene and eqtl
causal.pos.eqtlB[,("dist.A"):=start.A-eqtlA.pos]
causal.pos.eqtlB[,("dist.B"):=start.B-eqtlB.pos]

# plot distance between gene and eqtl (ordered by distance value)
plot(unique(causal.pos.eqtlB[,.(geneB, eqtl.B, dist.B)])[order(-dist.B)]$dist.B, pch=".", col="red",
     cex=2, ylab="distance", xlab="gene-eqtl pair", main="Distance between eqtl and gene")
legend("topright", legend=c("geneA-eqtlA", "geneB-eqtlB"),col=c("blue", "red"), lty=1, cex=0.8, inset=.02)
points(unique(causal.pos.eqtlB[,.(geneA, eqtl.A, dist.A)])[order(-dist.A)]$dist.A, pch=".", col="blue", cex=2)

14.0.2 Plot where (relative to the gene) the eqtls are located

# Get genes that have eqtls "inside" the gene
# unique(causal.pos.eqtlB[,.(geneA, eqtl.A, dist.A)])[order(-dist.A)][abs(dist.A) <= 100]

# negative distances mean that the eqtl is "inside" or after the gene
# positive numbers mean that the eqtl is before the gene 
# genesA that have an eqtl "inside"
distance.A <- unique(causal.pos.eqtlB[,.(geneA, eqtl.A, chr.A, start.A, end.A, eqtlA.chr,eqtlA.pos, dist.A)])
distance.B <- unique(causal.pos.eqtlB[,.(geneB, eqtl.B, chr.B, start.B, end.B, eqtlB.chr,eqtlB.pos, dist.B)])

# genes that have an eqtl "inside" the gene 
distA.inside <- distance.A[start.A < eqtlA.pos & eqtlA.pos < end.A][order(-dist.A)]
# genesA that have an eqtl before their start position
distA.before <- distance.A[start.A > eqtlA.pos][order(-dist.A)]
# genes that have an eqtl after their end position
distA.after <- distance.A[eqtlA.pos > end.A][order(-dist.A)]


# genes that have an eqtl "inside" the gene 
distB.inside <- distance.B[start.B < eqtlB.pos & eqtlB.pos < end.B][order(-dist.B)]
# genesA that have an eqtl before their start position
distB.before <- distance.B[start.B > eqtlB.pos][order(-dist.B)]
# genes that have an eqtl after their end position
distB.after <- distance.B[eqtlB.pos > end.B][order(-dist.B)]

### sanity check
comb.inside <- distA.inside %>% unite(gene_eqtl, geneA, eqtl.A, sep = "__")
comb.before <- distA.before %>% unite(gene_eqtl, geneA, eqtl.A, sep = "__")
comb.after <- distA.after %>% unite(gene_eqtl, geneA, eqtl.A, sep = "__")
if(any(comb.inside %in% comb.before | comb.inside %in% comb.after | comb.before %in% comb.after)){
  stop("Something's wrong with the gene-eqtl distance calculation")
}
###
# Plot number of genes that have eqtls before the gene start site, within the gene or after the gene's end site
toplotA <- c(before=nrow(distA.before), inside=nrow(distA.inside), after=nrow(distA.after))
toplotB <- c(before=nrow(distB.before), inside=nrow(distB.inside), after=nrow(distB.after))

toplotAB <- rbind(toplotA, toplotB)
bpAB <- barplot(toplotAB, main="Number of genes that have eqtls at each position relative to itself", col=c("darkblue","red"),
        legend = c("causal genes", "affected genes"), beside=TRUE, ylim=c(0,max(toplotAB)+50))
text(bpAB, toplotAB, labels=toplotAB, cex=1, pos=3)

# bpB <- barplot(toplotB, 
#               main = "Number of genes that have eqtls at each position relative to itself \n (causal genes)", 
#               ylim = c(0, max(toplotB)+50))
# text(bpB, toplotB, labels=toplotB, cex=1, pos=3)

toplotA <- c(before=nrow(distA.before), inside=nrow(distA.inside), after=nrow(distA.after))
bpA <- barplot(toplotA, main = "Number of genes that have eqtls at each position relative to itself \n (causal genes)", 
        ylim = c(0, max(toplotA)+50), col="darkblue")
text(bpA, toplotA, labels=toplotA, cex=1, pos=3)

Most of the eqtls seem to be located before the gene.

14.0.3 How many gene-eqtl pairs are not in the paper’s reported pairs

geneeqtlA.sub <- unique(find.effects_TF[,.(geneA, eqtl.A)])
geneeqtlB.sub <- unique(find.effects_TF[,.(geneB, eqtl.B)])
genepmarker.sub <- eqtl_results[,.(gene, pmarker)]

combinegeneeqtlA <- geneeqtlA.sub %>% unite(gene_eqtl, geneA, eqtl.A, sep = "__")
combinegeneeqtlB <- geneeqtlB.sub %>% unite(gene_eqtl, geneB, eqtl.B, sep = "__")
combine_genepmarker <- genepmarker.sub %>% unite(gene_eqtl, gene, pmarker, sep = "__")


geneA.eqtlA.notinpaper <- combinegeneeqtlA[which(!combinegeneeqtlA %in% combine_genepmarker)]
geneB.eqtlB.notinpaper <- combinegeneeqtlB[which(!combinegeneeqtlB %in% combine_genepmarker)]

There are 1 gene-eqtl pairs for the causal genes not in the provided gene-eqtl table.
There are 1 gene-eqtl pairs for the affected genes not in the provided gene-eqtl table